BACKGROUND OF THE INVENTION

Technical Field

This invention relates to self-adapting/self-adjusting neural network system. Observing a different environment/condition/situation under which the neural network system finds itself operating from a previously observed environment/condition/situation, the system automatically reconfigures one or more neural networks—reconfiguring may include using a different set of coefficients for a neural network running within the neural network system.

Description of the Related Art

As illustrated inFIG. 1, a conventional Neural Network (NN)101receives an input (a single vector, in this example) at an input layer102, and transforms it through a series of one or more hidden layers103. Each hidden layer includes a set of “neurons” or “nodes,” where each neuron is connected to all neurons in the previous layer (e.g., the input layer or another hidden layer), and where neurons in a single layer function completely independently and do not share any connections. The last fully-connected layer is called the “output layer”105, where each of the neurons in the output layer can provide a portion of the output information (or signal), and in classification applications the output layer information (or setting(s)) represents the class scores (e.g., the score of the input(s) being classified). Based on the class scores, an output of the neural network can be determined as the class having the highest score. In other neural networks, the output may consist of an indication of the class having the highest score as the one selected and a confidence level of the selected class for given input data being the correct one based on the relative scores with other classes. This confidence level is referred to as an output confidence level, herein.

As illustrated inFIG. 2, a “convolutional” Neural Network201can take 3D images as input, for instance. In particular, unlike the neural network described in connection withFIG. 1, the layers of a convolutional neural network have neurons arranged in 3 dimensions: width, height, depth. Note that the word depth here refers to the third dimension of an activation volume, not to the depth of a full neural network, which can refer to the total number of layers in a network. The neurons in a layer can be configured to be connected to a small region of the layer before it, instead of all of the neurons in a fully-connected manner. The convolutional neural network reduces the full image into a single vector of class scores203, arranged along the depth dimension.

Neural networks have been employed to operate in complex and widely varying settings (e.g., different environments, conditions, situations, and/or etc.). As such, ever increasing quantities of train data sets have been used to train prior art neural networks to operate in as many different settings as possible. When the size of training data set is increased to include training data samples from many different settings, a prior art neural network can begin to lose its accuracy and can encounter a catastrophic memory loss, which causes the neural network to cease to operate as it was originally designed and trained for.

SUMMARY OF THE INVENTION

Various aspects of the present invention includes inventive features and embodiments to allow neural network systems of the present invention to maintain or increase operational accuracy in controlling a machine while being able to operate in various, different settings. In particular, a set of training data is collected over each of at least two different settings (e.g., a setting can be an environment, a condition, a situation, or the like in/under which a machine is to operate). Each setting can have its own characteristics. In some embodiments, these characteristics can be defined using a set of ranges of values. Examples of types of characteristics for the settings can be time, geographical location, and/or weather condition, etc. Using the training data set, a neural network having a particular structure can be trained for a given setting, which results in a set of coefficients for the particular neural network. For each setting, the characteristics for the setting are associated with the corresponding coefficients and/or the corresponding neural network structure trained with the training data set collected in/under the setting. Information relating to the characteristics, coefficients, and neural network structures for various settings can be stored in a database management system.

Operating in/under a setting, a neural network that has the coefficients and neural network structure associated with a set of characteristics corresponding to the setting, the neural network would yield optimal results for which it is designed/trained. In operation, various characteristics of the setting are monitored since the machine can move into or the environment/condition/situation may change to a new setting. That is, the setting may change from one setting to a new setting—the neural network coefficients and/or neural network structure (or the neural network executable module having the structure and/or the coefficients) associated with the new setting can be retrieved from a database management system. A new neural network can then be instantiated with those coefficients and may become operational, while the old neural network becomes inactive (e.g., becomes non-operational or terminated). In other words, various embodiments of the present invention allow adaptively changing the neural network(s) based on changing settings (e.g., changes in environment, condition and/or situation).

First variations of methods of controlling a machine include a process (or steps of), without requiring a particular order or sequence, storing at least two sets of neural network coefficients, each being different from the others with one or more characteristics of a setting, associating each of the at least two sets of neural network coefficients with at least one set of one or more ranges of values, receiving first data from one or more input devices of the machine, selecting one from the at least two sets of neural network coefficients based on the first data and the at least one set of one or more ranges of values. The methods of the various embodiments may also include the steps of instantiating a neural network with the selected one from the at least two sets of neural network coefficients, and controlling an aspect of the machine using an output from the instantiated neural network. As indicated above, the use of “step” herein when referring to a portion of a process does not itself indicate any particular sequence or order of the process portions, unless otherwise indicated explicitly or as required by the context of the described process.

First variations of methods of controlling a machine may also include, without requiring a particular order or sequence, the steps of associating a plurality among the at least two sets of neural network coefficients with a second set of one or more ranges of values, and/or storing information relating to a neural network structure associated each of the at least two sets of neural network coefficients. The methods may further include the step of selecting one from the at least two sets of neural network further comprises the step of matching the first data with one of the at least one set of one or more ranges of values. The matching step can further comprises the steps of comparing the first data with the at least one set of one or more ranges of values; and identifying the selected one of the at least one set among one or more ranges of values that has the first data fall within its ranges of values, wherein the neural network coefficients matched with the selected one are generated by using training data set collected within the corresponding particular setting.

Second variations of methods of controlling a machine may include the steps of, without requiring a particular order or sequence, storing at least two sets of neural network coefficients, each being different from the others with one or more characteristics of a setting, associating each of the at least two sets of neural network coefficients with one or more characteristics of a setting, receiving first data from one or more input devices of the machine, selecting one from the at least two sets of neural network coefficients based on the first data and the one or more characteristics of settings, instantiating a neural network with the selected one from the at least two sets of neural network coefficients, and controlling an aspect of the machine using an output from the instantiated neural network. Wherein, each of the one or more characteristics of settings is defined with a range of values.

Second variations of methods of controlling a machine may also include the steps of, without requiring a particular order or sequence, storing information relating to a neural network structure associated each of the at least two sets of neural network coefficients. The step of selecting one from the at least two sets of neural network coefficients further comprises the step of matching the first data with the one or more characteristics of settings, which may further include the steps of comparing the first data with the one or more characteristics of settings, wherein each of the one or more characteristics of settings is defined with a range of values, and identifying the selected one of the one or more characteristics of settings that the first data fall within the ranges of values.

Various embodiments of methods of controlling a machine may further include, without requiring a particular order or sequence, the steps of storing a set of one or more input range values associated each of the at least two sets of neural network coefficients, comparing the first data with the one or more input range values associated with the selected one from the at least two sets of neural network coefficients, and selecting a new set among the at least two sets of neural network coefficients if the first data is outside the input range values. In other variation, the methods may include the steps of storing a set of one or more output range values associated each of the at least two sets of neural network coefficients, comparing the output with the one or more output range values associated with the selected one from the at least two sets of neural network coefficients, and selecting a new set among the at least two sets of neural network coefficients if the output is outside the output range values.

First variations of apparatuses of controlling a machine may include a database management system stored with at least two sets of neural network coefficients being different from each other, at least one set of one or more ranges of values with one or more characteristics of a setting, and each of the at least two sets of neural network coefficients being associated with at least one set of one or more ranges of values; and means for controlling coupled to receive first data from one or more input devices of the machine, wherein the means for controlling includes means for selecting one from the at least two sets of neural network coefficients based on the first data and the at least one set of one or more ranges of values, and means for instantiating a neural network with the selected one from the at least two sets of neural network coefficients, wherein the neural network is configured to generate an output being used to control an aspect of the machine.

Second variations of apparatuses of controlling a machine may include a database management system stored with at least two sets of neural network coefficients being different from each other with one or more characteristics of a setting, at least one set of one or more ranges of values, and each of the at least two sets of neural network coefficients being associated with at least one set of one or more ranges of values, and a controlling device that is coupled to receive first data from one or more input devices of the machine, arranged to select one from the at least two sets of neural network coefficients based on the first data and the at least one set of one or more ranges of values, and arranged to instantiate a neural network with the selected one from the at least two sets of neural network coefficients, wherein the neural network is configured to generate an output being used to control an aspect of the machine.

In the first and second variations of apparatuses, the database management system may further store a plurality among the at least two sets of neural network coefficients associated with a second set of one or more ranges of values, and information relating to a neural network structure associated each of the at least two sets of neural network coefficients. In these embodiments, the database management system can be configured to match the first data with one of the at least one set of one or more ranges of values, to compare the first data with the at least one set of one or more ranges of values, and to identify the selected one of the at least one set among one or more ranges of values that has the first data fall within its ranges of values.

Third variations of apparatuses of controlling a machine may include a database management system stored with at least two sets of neural network coefficients being different from each other, at least one setting having one or more characteristics with one or more characteristics of a setting, and each of the at least two sets of neural network coefficients being associated with the at least one setting having one or more characteristics, and a controlling device that is coupled to receive first data from one or more input devices of the machine, arranged to select one from the at least two sets of neural network coefficients based on the first data and at one least one setting having one or more characteristics, and arranged to instantiate a neural network with the selected one from the at least two sets of neural network coefficients, wherein the neural network is configured to generate an output being used to control an aspect of the machine.

Fourth variations of apparatuses of controlling a machine may include a database management system stored with at least two sets of neural network coefficients being different from each other with one or more characteristics of a setting, at least one setting having one or more characteristics, and each of the at least two sets of neural network coefficients being associated with the at least one setting having one or more characteristics, and means for, coupled to receive first data from one or more input devices of the machine, selecting one from the at least two sets of neural network coefficients based on the first data and at one least one setting having one or more characteristics, and instantiating a neural network with the selected one from the at least two sets of neural network coefficients, wherein the neural network is configured to generate an output being used to control an aspect of the machine.

In the third and fourth variations of apparatuses, the database management system may further store each of at least one setting having one or more characteristics is defined with a range of values. The database management system also can be configured to match the first data with one of at least one setting having one or more characteristics, and may be further configured to compare the first data with the at least one setting having one or more characteristics defined with a range of values and to identify the selected one of the at least one set among one or more ranges of values that has the first data fall within its ranges of values.

In variations of apparatuses of controlling a machine, the database management system can further store a set of one or more input range values associated each of the at least two sets of neural network coefficients and the instantiated neural network with the selected one from the at least two sets of neural network coefficients, and the apparatuses can further include a first trigger event detector arranged to compare the first data with the one or more input range values associated with the selected one from the at least two sets of neural network coefficients and to send a signal to the controlling device to select a new set among the at least two sets of neural network coefficients if the first data is outside the input range values. The database management system can also store a set of one or more output range values associated each of the at least two sets of neural network coefficients and the instantiated neural network with the selected one from the at least two sets of neural network coefficients, and further includes a second trigger event detector arranged to compare the output with the one or more output range values associated with the selected one from the at least two sets of neural network coefficients and to send a signal to the controlling device to select a new set among the at least two sets of neural network coefficients if the output is outside the output range values.

Another innovation includes a method of controlling a machine using predictive information, the method comprising storing at least two sets of neural network coefficients, each being different from the other, receiving first data from one or more input devices of the machine; selecting one from the at least two sets of neural network coefficients based on the first data and the predictive information, instantiating a neural network with the selected one from the at least two sets of neural network coefficients, conducting nodal operations at nodes of the instantiated neural network, and controlling an aspect of the machine using an output from the instantiated neural network. Such methods can include other features and aspects described herein and illustrated in the figures.

For example, in some embodiments the method further comprises associating each of the at least two sets of neural network coefficients with one or more characteristics of a setting, wherein the one or more characteristics relates to the predictive information of said setting. Each of the one or more characteristics of a setting may be defined with a range of values. The method may further comprise storing information relating to a neural network structure associated each of the at least two sets of neural network coefficients. In some embodiments, the neural network structure may be one of a convolutional neural network, a feed forward neural network, a neural Turing machine, Hopfield neural network, or a Boltzmann machine neural network. In some embodiments, the setting is one of a temperate urban region, a desert rural region, a forested mountain region, or a coastal city. In various embodiments, selecting one from the at least two sets of neural network coefficients further comprises matching the first data with the one or more characteristics of settings. In some embodiments, the matching further comprises comparing the first data with the one or more characteristics of settings, wherein each of the one or more characteristics of settings is defined with a range of values, and identifying the selected one of the one or more characteristics of settings that the first data fall within the range of values. In some embodiments, the neural network coefficients matched with the selected one are generated by using training data set collected within the corresponding particular setting. In some embodiments, the first data includes data from a Global Positioning System (GPS). In some embodiments, the information relating to the at least two sets of neural network coefficients is stored in a standardized format to allow access by electronic devices manufactured by different manufacturers.

Another innovation includes a machine controller using predictive information, the machine controller comprising a computer memory that stores at least two sets of neural network coefficients, each being different from the other, an input interface device that receives first data from one or more input devices of the machine, wherein the machine controller is implemented to select one from the at least two sets of neural network coefficients based on the first data and predictive information, and a neural network that is instantiated with the selected one from the at least two sets of neural network coefficients, wherein the neural network conducts nodal operations at nodes, wherein the machine controller controls an aspect of the machine using an output from the neural network. In some embodiments, the controller is implemented to associate each of the at least two sets of neural network coefficients with one or more characteristics of a setting, and wherein the one or more characteristics relates to the predictive information of said setting. In some embodiments, each of the one or more characteristics of a setting may be defined with a range of values. In some embodiments, the controller further comprises a second memory that stores information relating to a neural network structure associated each of the at least two sets of neural network coefficients. In some embodiments, the neural network structure may be one of a convolutional neural network, a feed forward neural network, a neural Turing machine, Hopfield neural network, or a Boltzmann machine neural network. In some embodiments, the setting may be one of a temperate urban region, a desert rural region, a forested mountain region, and a coastal city.

Another innovation includes a method of controlling a machine, the method comprising storing at least two sets of neural network coefficients, each being different from the other, receiving first data from one or more input devices of the machine, selecting one from the at least two sets of neural network coefficients based on the first data, instantiating a neural network with the selected one from the at least two sets of neural network coefficients, conducting nodal operations at nodes of the instantiated neural network, and controlling an aspect of the machine using an output from the instantiated neural network. In some embodiments, the method further comprises associating each of the at least two sets of neural network coefficients with one or more characteristics of a setting, wherein the one or more characteristics relates to predicting information of said setting. In some embodiments, the method further comprises storing a set of one or more output range values associated each of the at least two sets of neural network coefficients, comparing the output with the one or more output range values associated with the selected one from the at least two sets of neural network coefficients, and selecting a new set among the at least two sets of neural network coefficients if the output is outside the output range values. In some embodiments, the method further comprises generating the set of one or more output range to be within a safe operational range of the aspect of the machine. In some embodiments, the method further comprises generating the set of one or more output range to be within a tolerable error range of the aspect of the machine. In some embodiments, the method further comprises generating the set of one or more output range to be within an optimal operational range of the aspect of the machine

Another innovation includes a machine controller, comprising a memory that stores at least two sets of neural network coefficients, each being different from the other, an input interface is implemented to receives first data from one or more input devices of the machine, wherein the controller is implemented to select one from the at least two sets of neural network coefficients based on the first data, and a neural network is instantiated with the selected one from the at least two sets of neural network coefficients, wherein the neural network conducts nodal operations at nodes, wherein the controller controls an aspect of the machine using an output from the neural network. In some embodiments, the controller is implemented to associate each of the at least two sets of neural network coefficients with one or more characteristics of a setting, wherein the one or more characteristics relates to predicting information of said setting.

Another aspect of innovation includes using various automated machines in groups to identify an optimal neural network in achieving a particular goal. More specifically, this aspect of the innovation includes a method of controlling a first group of automated machines and a second group of automated machines. The method includes the steps of controlling each of the first group of automated machines using a first set of neural network coefficients for achieving a common goal, controlling each of the second group of the automated machines using a second set of neural network coefficients for achieving said common goal, changing, for a plurality of automated machines among the second group of automated machines, from the second set of neural network coefficients to the first set of coefficients while operating the plurality of automated machines among the second group, and then controlling the plurality of automated machines among the second group using the first set of neural network coefficients. A “goal” when used herein, is a broad term and may correspond to a desired classification or calculated result, or sets of results. For example, if a goal is for a group of drones to intercept another vehicle or moving platform, the goal may correspond to certain neural network configurations and results relating to one or more of speed, heading, time it took to intercept, speed of calculations (e.g., using floating point of integer calculations), accuracy of intercept (e.g., determined by location of an intercept point to a threshold), and the like, that are determined from the different configurations.

The method may also include the steps of generating first results by operating the first group of machines and generating second results by operation the second group of machines, evaluating the first results with the second results based on a criterion. The above mentioned executing step of changing from the second set of neural network coefficients to the first set of coefficients, if the first results are better compared with the second results based on the criteria.

The innovation may also include an automated machine configured to operate along with other automated machines to achieve a common goal. Such an automated machine may include a first neural network having a first structure and a first set of coefficients, a second neural network having a second structure and a second set of coefficients, and a controller device implemented to operate an aspect of the automated machine using the first neural network and to change over to using the second neural network while the automated machine is in operation upon receiving information that the second neural network yields better results in achieving the common goal. The automated machine may also include a receiver implemented to receive from a remote device the information that the second neural network yield better results in achieving the common goal. Such an automated machine can be a drone, a cell phone, or a device to provide cyber security. The common goal can be arriving at a target location, receiving or transmitting data speedily with a minimal error rate, or providing security for a computer network.

For the various preferred embodiments, the neural network structure can be one of a convolutional neural network, a feed forward neural network, a neural Turing machine, Hopfield neural network, and Boltzmann machine neural network. In these embodiments, a setting can be one of a temperate urban region, a desert rural region, a forested mountain region, and a coastal city and/or can be one of environment, condition, and situation in/under which the machine operates, and the information relating to the at least two sets of neural network coefficients is stored in a standardized format to allow access by electronic devices manufactured by different manufacturers or electronic devices belonging to different manufacturing entities. Also, the neural network coefficients matched with the selected one can be generated by using training data set collected within the corresponding setting.

Another aspect of the present invention may include a controller for an autonomous machine coupled to a plurality of sensors generating input data. The controller comprises a) a first neural network deployed on the autonomous machine, trained with a first training data set and configured to generate first output data after processing a set of input data, b) a second neural network deployed on the autonomous machine, trained with a second training data set and configured to generate second output data after processing said set of input data, wherein the first and second neural networks being executed simultaneously; and c) a comparator receiving and comparing the first output data and second output data, wherein the controller controls the autonomous machine using input that includes the first output data and a result of the comparator. It should be noted that the comparator can be further configured to detect a minimum difference between the first and second output data. The controller can also include a counter for counting instances of the comparator detecting the minimum difference between the first and second output data. The controller can stop using the first output data to control the autonomous machine when the counter counts more than a predetermined number during a predetermined time period. In some embodiments, the first and second training data sets are identical to each other. The controller can be further constructed to use the second output data to control the autonomous machine when the counter counts more than the predetermined number during the predetermined time period.

Yet another aspect of the present invention relates to a method of controlling an autonomous machine coupled to a plurality of sensors generating input data. The method may include: a) inferencing to generate first output data on a first neural network deployed on the autonomous machine and trained with a first training data set, b) inferencing to generate second output data on a second neural network deployed on the autonomous machine and trained with a second training data set, wherein the first output data and second output data are being generated simultaneously, c) comparing the first output data and second output data; and d) controlling the autonomous machine using input that includes the first output data and a result of the comparing step. The method may also include the steps of detecting a minimum difference between the first and second output data and counting the detection of the minimum difference between the first and second output data, wherein the controller stops using the first output data to control the autonomous machine when the counting steps results more than a predetermined number during a predetermined time period. Here, the controlling step can use the second output data to control the autonomous machine when the counter counts more than the predetermined number during the predetermined time period.

In some embodiments, the predetermined number can be set at one. In some embodiments, the predetermined time period can be set at one millisecond. In some embodiments, the first and second output data can be numerical values. In such embodiments, the minimum difference can be 0.1% of an average of the numerical values. In other words, ins some examples the minimum difference can be a very small non-zero percentage.

