11056640
Two Further Gradient BYY Learning Rules for Gaussian Mixture with Automated Model Selection
Under the Bayesian Ying-Yang (BYY) harmony learning theory, a harmony function has been developed for Gaussian mixture model with an important feature that, via its maximization through a gradient learning rule, model selection can be made automatically during parameter learning on a set of sample data from a Gaussian mixture. This paper proposes two further gradient learning rules, called conjugate and natural gradient learning rules, respectively, to efficiently implement the maximization of the harmony function on Gaussian mixture. It is demonstrated by simulation experiments that these two new gradient learning rules not only work well, but also converge more quickly than the general gradient ones.
18710459
Conjugate and natural gradient rules for BYY harmony learning on Gaussian mixture with automated model selection
Under the Bayesian Ying–Yang (BYY) harmony learning theory, a harmony function has been developed on a BI-directional architecture of the BYY system for Gaussian mixture with an important feature that, via its maximization through a general gradient rule, a model selection can be made automatically during parameter learning on a set of sample data from a Gaussian mixture. This paper further proposes the conjugate and natural gradient rules to efficiently implement the maximization of the harmony function, i.e. the BYY harmony learning, on Gaussian mixture. It is demonstrated by simulation experiments that these two new gradient rules not only work well, but also converge more quickly than the general gradient ones.
59364798
The modeling of piezoceramic patch interactions with shells, plates, and beams
Abstract : General models describing the interactions between a pair of piezoceramic patches and elastic substructures consisting of a cylindrical shell, plate and beam are presented. In each case, the manner in which the patch loads enter both the strong and weak forms of the time-dependent structural equations of motion is described. Through force and moment balancing, these loads are then determined in terms of material properties of the patch and substructure (thickness, elastic properties, Poisson ratios), the geometry of the patch placement, and the voltages into the patches. In the case of the shell, the coupling between bending and inplane deformations, which is due to the curvature, is retained. These models are sufficiently general to allow for potentially different patch voltages which implies that they can be suitably employed when using piezoceramic patches for controlling system dynamics when both extensional and bending vibrations are present.
19552464
Attribute reduction and optimal decision rules acquisition for continuous valued information systems
For continuous valued information systems, the attribute values of objects for the same attribute represent not only their ordinal relationship but also their relative distances. Therefore, the classical rough set model is not suitable for deducing attribute reductions and optimal decision rules for continuous valued information systems. Though some discretization methods are proposed to transform the continuous valued information systems into discrete ones, but those methods are too categorical and may lead to loss of information in some cases. To solve such information loss problem, we propose a tolerance rough set model in this paper. With a given level, the proposed model can divide a universe into some maximal tolerance classes. Also two types of lower and upper approximations are defined accordingly. Then the reductions of the maximal tolerance class and optimal decision rules based on the proposed attribute descriptors are defined, and the approximate discernibility function for the maximal tolerance class is constructed and used to compute all the corresponding optimal decision rules via using Boolean reasoning techniques. Finally, the general reductions and consistent reductions for continuous valued information systems are discussed.
36297757
Density Prediction of Selective Laser Sintering Parts Based on Artificial Neural Network
Selective laser sintering (SLS), one of rapid prototyping technologies, employs laser beam to selectively fuse fully powder into a solid object layer by layer. However, density prediction of SLS parts using finite elements analysis (FEA) having been reported, heavily depends on the precision of the FEA model. An Artificial neural network (ANN) approach presented in this paper has been developed for density prediction of SLS parts. Two-layer supervised neural networks are used, and the inputs to the neural network are known SLS process parameters such as laser power, scan speed, scan spacing and layer thickness. Orthogonal experimental method is employed for collection of experimental training and test sets. The construction of network is also investigated. Comparison of predicted and experimental data has confirmed the accuracy of the ANN approach.
15223911
Boosting with incomplete information
In real-world machine learning problems, it is very common that part of the input feature vector is incomplete: either not available, missing, or corrupted. In this paper, we present a boosting approach that integrates features with incomplete information and those with complete information to form a strong classifier. By introducing hidden variables to model missing information, we form loss functions that combine fully labeled data with partially labeled data to effectively learn normalized and unnormalized models. The primal problems of the proposed optimization problems with these loss functions are provided to show their close relationship and the motivations behind them. We use auxiliary functions to bound the change of the loss functions and derive explicit parameter update rules for the learning algorithms. We demonstrate encouraging results on two real-world problems --- visual object recognition in computer vision and named entity recognition in natural language processing --- to show the effectiveness of the proposed boosting approach.
35749769
EFSM-based Testing Strategy for APIs Test of Embedded OS
In this paper, we present a two-phase approach to generate test data for the EFSM model of an embedded operating system. We first build the EFSM model of the system. The model can then be used to automatically generate test data for testing of APIs. We also point out certain issues that need further study.