207558887
Self-adaptive neuro-fuzzy systems for autonomous underwater vehicle control
This paper presents a systematic approach for developing a concise self-adaptive neurofuzzy inference system (SANFIS) with a fast hybrid parameter learning algorithm for on-line learning of the control knowledge for autonomous underwater vehicle (AUV) control. The multi-layered network structure of SANFIS incorporates fuzzy basis functions for better function approximations. We investigate three SANFIS structures with three different types of fuzzy IF-THEN rule-based models and cast the rule formation problem as a clustering problem. A recursive least-squares algorithm and a modified Levenberg-Marquardt algorithm with limited memory are exploited to accelerate the parameter learning process. This hybrid parameter learning algorithm together with an on-line clustering technique and rule examination provide SANFIS with the capability of selforganizing and self-adapting its internal structure (i.e. the fuzzy rules and term sets) for learning the required control knowledge for an AUV to follow desired trajectories. Computer simulations for modeling a control system for an AUV have been conducted to validate the effectiveness of the proposed SANFIS.
30064492
Genetic reinforcement learning approach to the machine scheduling problem
This paper focuses on the development of a learning-based heuristic for the machine scheduling problem, which automatically captures the search control knowledge or the common features of good schedules while generating a number of schedules. Defining states and actions of the machine shop, the machine scheduling problem is transformed into a problem of reinforcement learning (RL) in which a learner or a scheduler will learn to select the right action at each state of the machine shop, using the reward from a schedule evaluator for executing the action. Implementing the proposed reinforcement learning with a genetic algorithm results in the genetic reinforcement learning (GRL) approach to the machine scheduling problem. Although the learning-based heuristic has the overhead of acquiring knowledge on the problem, it can be easily adapted for a wide variety of machine scheduling problems due to the weak dependence on the problem structures and objectives. A GRL-based scheduler, called EVIS (Evolutionary Intracell Scheduler), has been developed and applied to various classes of machine scheduling problems, such as the job-shop scheduling, the flow-shop scheduling and the open-shop scheduling problems, and even the processor scheduling problem, the performance evaluation of EVIS with a number of different problem instances has shown that the learning-based heuristic is robust and its performance is comparable with that of other problem-specific heuristics or search-oriented heuristics in the quality of solutions.
41633235
Automatic generation of error recovery knowledge through learned reactivity
Develops a new method for automatic generation of error recovery procedures in the assembly workcell. Current reactive systems have integrated planning and learning capabilities to allow them to augment their store of reactive behaviors whenever they are forced to plan. The cycle of evaluation and reaction used by these systems implicitly detects and corrects any errors made in prior behavior execution. By capitalizing on this analogy to error recovery, the authors are able to gain the benefits of a reactive system for use in error recovery while minimizing the perception and evaluation burden that is inherent in those approaches. The authors present a system that uses the new learning reactive paradigm to quickly correct known errors and autonomously learn to recover new errors without human intervention.<<ETX>>
12503943
Model-based visual feedback control for a hand-eye coordinated robotic system
The integration of a single camera into a robotic system to control the relative position and orientation between the robot's end-effector and a moving part in real time is discussed. Only monocular vision techniques are considered because of current limitations in the speed of computer vision analysis. The approach uses geometric models of both the part and the camera, as well as the extracted image features, to generate the appropriate robot control signals for tracking. Part and camera models are also used during the teaching stage to predict important image features that appear during task completion.<<ETX>>
17815023
Experimental study on adaptive control of underwater robots
The control of underwater robots presents a number of unique and formidable challenges. Underwater robot dynamics are highly nonlinear, coupled, and time-varying, and subject to hydrodynamic uncertainties and external disturbances such as current. Unlike land mobile robots, underwater robots cannot use GPS. Most popular underwater positioning sensors are sonar-based, such as long-base line. However, autonomous processing of sonar measurements is plagued by noise, drop-outs, missed detection, false reading, poor resolution, etc. The paper presents a new adaptive control of underwater robots with sonar-based position measurements. Experimental results show robustness of the control system in the presence of unmodelled dynamics and various noise.
1173830
A genetic-algorithm-based neighbor-selection strategy for hybrid peer-to-peer networks
BitTorrent is a popular, open-source, hybrid peer-to-peer content distribution system that is conducive for distribution of large-volume contents. In this paper, we propose a genetic-algorithm-based neighbor-selection strategy for hybrid peer-to-peer networks, which enhances the decision process performed at the tracker for transfer coordination. We also investigate how the strategy affects system throughput and distribution efficiency as well as peer contributions. We show through computer simulations that by increasing content availability to the clients from their immediate neighbors, it can significantly improve the system performance without trading off users' satisfaction. The proposed strategy can significantly improve the efficiency of distribution, especially for low-connectivity peers, and it is suitable to deploy for online decisions
9595362
Genetic-based fuzzy image filter and its application to image processing
In this paper, we propose a Genetic-based Fuzzy Image Filter (GFIF) to remove additive identical independent distribution (i.i.d.) impulse noise from highly corrupted images. The proposed filter consists of a fuzzy number construction process, a fuzz filtering process, a genetic learning process, and an image knowledge base. First, the fuzzy number construction process receives sample images or the noise-free image and then constructs an image knowledge base for the fuzzy filtering process. Second, the fuzzy filtering process contains a parallel fuzzy inference mechanism, a fuzzy mean process, and a fuzzy decision process to perform the task of noise removal. Finally, based on the genetic algorithm, the genetic learning process adjusts the parameters of the image knowledge base. By the experimental results, GFIF achieves a better performance than the state-of-the-art filters based on the criteria of Peak-Signal-to-Noise-Ratio (PSNR), Mean-Square-Error (MSE), and Mean-Absolute-Error (MAE). On the subjective evaluation of those filtered images, GFIF also results in a higher quality of global restoration.
31930663
Minimization of resource consumption for multidatabase query optimization
Due to the autonomy of individual databases, a multidatabase system (MDBS) has no control over the task execution within each database system. This complicates the issue of query optimization in a MDBS. Past researchers tackled this problem mainly by regenerating the cost model of each participating database systems. However a completely autonomous participating database system does nor own the information (data) as well as the mechanism (software) for converting data of one database to another to resolve the data type conflict problems that can occur in any MDBS environment. In addition, unpredictable factors that can drastically deviate the accuracy of a regenerated cost model (in the past research) come into play at unknown time. This confines the processing of an intersite operation (such as a join over two relations of different databases) to the MDBS only. The participating database systems will not be able to share the workload of the MDBS under this circumstance. Hence, minimizing the consumption of system resources of the MDBS is an urgent problem. The authors propose three scheduling algorithms that are used in the MDBS to reduce the processing cost of a multidatabase query. A major difference between the strategies and the past methods is that ours do not require the knowledge of the cost models of the participating databases.
39910365
An intelligent image agent based on soft-computing techniques for color image processing
An intelligent image agent based on soft-computing techniques for color image processing is proposed in this paper. The intelligent image agent consists of a parallel fuzzy composition mechanism, a fuzzy mean related matrix process and a fuzzy adjustment process to remove impulse noise from highly corrupted images. The fuzzy mechanism embedded in the filter aims at removing impulse noise without destroying fine details and textures. A learning method based on the genetic algorithm is adopted to adjust the parameters of the filter from a set of training data. By the experimental results, the intelligent image agent achieves better performance than the state-of-the-art filters based on the criteria of Peak-Signal-to-Noise-Ratio (PSNR) and Mean-Absolute-Error (MAE). On the subjective evaluation of those filtered images, the intelligent image agent also results in a higher quality of global restoration.
8218476
A resource allocation model for QoS management
Quality of service (QoS) has been receiving wide attention in many research communities including networking, multimedia systems, real-time systems and distributed systems. In large distributed systems such as those used in defense systems, on-demand service and inter-networked systems, applications contending for system resources must satisfy timing, reliability and security constraints as well as application-specific quality requirements. Allocating sufficient resources to different applications in order to satisfy various requirements is a fundamental problem in these situations. A basic yet flexible model for performance-driven resource allocations can therefore be useful in making appropriate tradeoffs. We present an analytical model for QoS management in systems which must satisfy application needs along multiple dimensions such as timeliness, reliable delivery schemes, cryptographic security and data quality. We refer to this model as Q-RAM (QoS-based Resource Allocation Model). The model assumes a system with multiple concurrent applications, each of which can operate at different levels of quality based on the system resources available to it. The goal of the model is to be able to allocate resources to the various applications such that the overall system utility is maximized under the constraint that each application can meet its minimum needs. We identify resource profiles of applications which allow such decisions to be made efficiently and in real-time. We also identify application utility functions along different dimensions which are composable to form unique application requirement profiles. We use a video-conferencing system to illustrate the model.
2875607
An exact stochastic analysis of priority-driven periodic real-time systems and its approximations
This paper describes a stochastic analysis framework which computes the response time distribution and the deadline miss probability of individual tasks, even for systems with a maximum utilization greater than one. The framework is uniformly applied to fixed-priority and dynamic-priority systems and can handle, tasks with arbitrary relative deadlines and execution time distributions.