The architecture and learning procedure underlying ANFIS (adaptive-network-based fuzzy inference system) is presented, which is a fuzzy inference system implemented in the framework of adaptive networks. By using a hybrid learning procedure, the proposed ANFIS can construct an input-output mapping based on both human knowledge (in the form of fuzzy if-then rules) and stipulated input-output data pairs. In the simulation, the ANFIS architecture is employed to model nonlinear functions, identify nonlinear components on-line in a control system, and predict a chaotic time series, all yielding remarkable results. Comparisons with artificial neural networks and earlier work on fuzzy modeling are listed and discussed. Other extensions of the proposed ANFIS and promising applications to automatic control and signal processing are also suggested. >

ANFIS: adaptive-network-based fuzzy inference system

A review on image segmentation techniques

Learning and evolution are two fundamental forms of adaptation. There has been a great interest in combining learning and evolution with artificial neural networks (ANNs) in recent years. This paper: 1) reviews different combinations between ANNs and evolutionary algorithms (EAs), including using EAs to evolve ANN connection weights, architectures, learning rules, and input features; 2) discusses different search operators which have been used in various EAs; and 3) points out possible future research directions. It is shown, through a considerably large literature review, that combinations between ANNs and EAs can lead to significantly better intelligent systems than relying on ANNs or EAs alone.

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Evolving artificial neural networks

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Combining Pattern Classifiers

The performance of the MUSIC and ML methods is studied, and their statistical efficiency is analyzed. The Cramer-Rao bound (CRB) for the estimation problems is derived, and some useful properties of the CRB covariance matrix are established. The relationship between the MUSIC and ML estimators is investigated as well. A numerical study is reported of the statistical efficiency of the MUSIC estimator for the problem of finding the directions of two plane waves using a uniform linear array. An exact description of the results is included. >

MUSIC, maximum likelihood, and Cramer-Rao bound

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Adaptive pattern recognition and neural networks

A theoretical justification for the random vector version of the functional-link (RVFL) net is presented in this paper, based on a general approach to adaptive function approximation The approach consists of formulating a limit-integral representation of the function to be approximated and subsequently evaluating that integral with the Monte-Carlo method Two main results are: (1) the RVFL is a universal approximator for continuous functions on bounded finite dimensional sets, and (2) the RVFL is an efficient universal approximator with the rate of approximation error convergence to zero of order O(C//spl radic/n), where n is number of basis functions and with C independent of n Similar results are also obtained for neural nets with hidden nodes implemented as products of univariate functions or radial basis functions Some possible ways of enhancing the accuracy of multivariate function approximations are discussed >

Stochastic choice of basis functions in adaptive function approximation and the functional-link net

In the post-fault dynamic analysis of interconnected power systems, the critical fault clearing time (CCT) is one of the parameters of paramount importance. Critical clearing time is a complex function of pre-fault system conditions (operating point, topology, system parameters), fault structure (type and location) and post-fault conditions that are in part dependent on the protective relaying policy. To define analytically such a relationship would be highly desirable but diversity of variable involved makes this task extremely complicated. Our efforts focus on examination of that complex mapping and investigation of the influence of the various parameters on CCT. The evaluation of CCT involves elaborate computations that often include time-consuming solutions of nonlinear on-fault system equations. Existing conventional pattern recognition techniques are incapable of synthesizing such complex and transparent mappings. Thus, when a human operator tells the machine learning unit (that is the pattern recognizer) that system state belongs to a certain class, say "emergency", the pattern recognizer merely records that classification mindlessly and is not able to look at the pattern with insight and discover what underlies the "emergency" nature of pattern. It is, therefore, highly desirable to have a mechanism which when presented with a sequence of class labeled patterns not only learns an internal structure which allows it to generalize and to classify other and to classify other patterns correctly, but also is able to shed some light on what combination of features give rise to the particular class membership.

Artificial neural-net based dynamic security assessment for electric power systems

Feasible approaches to the task of solving NP-complete problems usually entails the incorporation of heuristic procedures so as to increase the efficiency of the methods used. We propose a new technique, which incorporates the idea of simulated annealing into the practice of simulated evolution, in place of arbitrary heuristics. The proposed technique is called guided evolutionary simulated annealing (GESA). We report on the use of GESA approach primarily for combinatorial optimization. In addition, we report the case of function optimization, treating the task as a search problem. The traveling salesman problem is taken as a benchmark problem in the first case. Simulation results are reported. The results show that the GESA approach can discover a very good near optimum solution after examining an extremely small fraction of possible solutions. A very complicated function with many local minima is used in the second case. The results in both cases indicate that the GESA technique is a practicable method which yields consistent and good near optimal solutions, superior to simulated evolution. >

Combinatorial optimization with use of guided evolutionary simulated annealing

It is highly desirable that the security and stability of electric power systems after exposure to large disturbances be assessable. In this connection, the critical clearing time (CCT) is an attribute which provides significant information about the quality of the post-fault system behavior. It may be regarded as a complex mapping of the prefault, fault-on, and post-fault system conditions in the time domain. Y.-H. Pao and D.J. Solajic (1989) showed that a feedforward neural network can be used to learn this mapping and successfully perform under variable system operating conditions and topologies. In that work the system was described in terms of some conventionally used parameters. In contrast to using those pragmatic features selected on the basis of the engineering understanding of the problem, the possibility of using unsupervised and supervised learning paradigms to discover what combination of raw measurements are significant in determining CCT is considered. Correlation analysis and Euclidean metric are used to specify interfeature dependencies. An example of a 4-machine power system is used to illustrate the suggested approach. >

Yoh-Han Pao

Papers

Adaptive pattern recognition and neural networks

Stochastic choice of basis functions in adaptive function approximation and the functional-link net

Artificial neural-net based dynamic security assessment for electric power systems

Combinatorial optimization with use of guided evolutionary simulated annealing

Combined use of unsupervised and supervised learning for dynamic security assessment