A general neural-network (connectionist) model for fuzzy logic control and decision systems is proposed. This connectionist model, in the form of feedforward multilayer net, combines the idea of fuzzy logic controller and neural-network structure and learning abilities into an integrated neural-network-based fuzzy logic control and decision system. A fuzzy logic control decision network is constructed automatically by learning the training examples itself. By combining both unsupervised (self-organized) and supervised learning schemes, the learning speed converges much faster than the original backpropagation learning algorithm. The connectionist structure avoids the rule-matching time of the inference engine in the traditional fuzzy logic system. Two examples are presented to illustrate the performance and applicability of the proposed model. >

Neural-network-based fuzzy logic control and decision system

A neural network with a single layer of hidden units of gaussian type is proved to be a universal approximator for real-valued maps defined on convex, compact sets of Rn.

Layered neural networks with Gaussian hidden units as universal approximations

A Gaussian potential function network with hierarchically self-organizing learning

One popular class of unsupervised algorithms are competitive algorithms. In the traditional view of competition, only one competitor, the winner, adapts for any given case. I propose to view competitive adaptation as attempting to fit a blend of simple probability generators (such as gaussians) to a set of data-points. The maximum likelihood fit of a model of this type suggests a "softer" form of competition, in which all competitors adapt in proportion to the relative probability that the input came from each competitor. I investigate one application of the soft competitive model, placement of radial basis function centers for function interpolation, and show that the soft model can give better performance with little additional computational cost.

/pdf/maximum-likelihood-competitive-learning-1uiwlb5lmy.pdf

Maximum Likelihood Competitive Learning

A comprehensive classifier system is presented for short-duration oceanic signals obtained from passive sonar, which exhibit variability in both temporal and spectral characteristics even in signals obtained from the same source. Wavelet-based feature extractors are shown to be superior to the more commonly used autoregressive coefficients and power spectral coefficients for describing these signals. A variety of static neural network classifiers are evaluated and are shown to compare favorably with traditional statistical techniques for signal classification. The focus is on those networks that are able to time-out irrelevant input features and are less susceptible to noisy inputs, and two new neural-network-based classifiers are introduced. Methods for combining the outputs of several classifiers to yield a more accurate labeling are proposed and evaluated. These methods lead to higher classification accuracy and provide a mechanism for recognizing deviant signals and false alarms. Performance results are given for signals in the DARPA standard data set I. >

/pdf/a-neural-network-based-hybrid-system-for-detection-2ieus1lwqm.pdf

A neural network based hybrid system for detection, characterization, and classification of short-duration oceanic signals

The authors present a multilayer feedforward network, called the Gaussian potential function network (GPFN), performing association or classification based on a set of potentially fields synthesized over the domain of input space by a number of Gaussian potential function units (GPFUs). A GPFU as a basic component of the GPFN is designed to generate a Gaussian form of a potential field. A weighted summation of Gaussian potential fields generated by a suitable number of GPFUs provides an arbitrary shape of a potential field over the domain of input space. The authors also present a detailed learning algorithm for the GPFN. Learning consists of the determination of the minimally necessary number of GPFUs and the adjustment of the locations and shapes of the individual Gaussian potential fields defined by GPFUs as well as the summation weights. The learning of the minimally necessary number of GPFUs is based on the control of the effective radius of GPFUs, while the parameter learning is based on the gradient descent procedure. >

Multilayer feedforward potential function network

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Multilayer feedforward potential function network