Preface to the Special Issue on SoftComputing for Modeling, Simulation, andControl of Nonlinear Dynamical SystemsOscar Castillo,* Patricia Melin†

Department of Computer Science, Tijuana Institute of Technology,Tijuana, Mexico

Soft computing can be used to build hybrid intelligent systems for achievingdifferent goals in real-world applications. Soft computing techniques include, atthe moment, fuzzy logic, neural networks, genetic algorithms, chaos theory meth-ods, and similar techniques that have been proposed in recent years. Each of thesetechniques has advantages and disadvantages, and several real-world problemshave been solved by using one of these techniques. However, many real-worldcomplex problems require the integration of several of these techniques to reallyachieve the efficiency and accuracy needed in practice. In particular, genetic algo-rithms can be used to optimize the topology of a fuzzy or a neural system. Also,there are neuro-fuzzy approaches or even neuro-fuzzy-genetic approaches fordesigning the best intelligent system for a particular application. With all of this inmind, we did organize this Special Issue to show the most recent research resultsof this type of work.

This special issue consists of nine articles that consider the use and integra-tion of different soft computing techniques for the development of hybrid intelli-gent systems for modeling, simulation, and control of nonlinear dynamical systems.The nine articles of this special issue describe different applications of soft com-puting techniques to real-world problems and can be considered a significant con-tribution to the field of hybrid intelligent systems.

The first article, “Hybrid Learning Architecture for Fuzzy Control of Quad-ruped Walking Robots” by Huosheng Hu and Dongbing Gu, deals with a newapproach combining reactive behaviors with deliberative reasoning to achieve com-plex goals in uncertain and dynamic environments. To achieve real-time and robustcontrol performance, fuzzy logic controllers are used to encode the behaviors and

*Author to whom all correspondence should be addressed: e-mail: ocastillo@tectijuana.mx.†e-mail: pmelin@tectijuana.mx.

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, VOL. 20, 127–129 (2005)© 2005 Wiley Periodicals, Inc. Published online in Wiley InterScience(www.interscience.wiley.com). • DOI 10.1002/int.20057

a two-stage learning scheme is adopted to make these controllers be adaptive tocomplex situations. Experimental results show the suitability of the architectureand effectiveness of the proposed learning approach.

The second article, “Robust Adaptive Fuzzy Control for Permanent MagnetSynchronous Servomotor Drives” by Yansheng Yang and Changjiu Zhou, dealswith a new robust adaptive fuzzy control algorithm for servomotor drives withuncertain nonlinearities and time-varying uncertainties. Takagi–Sugeno-type fuzzylogic systems are used to approximate uncertain functions. The closed-loop sys-tem is proven to be semiglobally uniformly ultimately bounded. The proposedmethodology is applied to design the position control of the permanent magnetsynchronous servomotor drives. Simulation results show the effectiveness of theproposed control scheme.

The third article, “Adaptive Recurrent Neural Network Control of BiologicalWastewater Treatment” by Ieroham S. Baruch et al., describes three adaptive neu-ral network control structures to regulate a biological wastewater treatment pro-cess. These methods are applied to achieve the goal of keeping the concentrationof the recycled biomass proportional to the influent flow rate in the presence ofperiodically acting disturbances, process parameter variations, and measurementnoise. Comparative simulation results confirmed the applicability of the proposedcontrol schemes.

The fourth article, “Hybrid Fuzzy Control of the Inverted Pendulum via Ver-tical Forces” by Darío Maravall et al., describes the effect of the vertical force asregards the stabilization of the inverted pendulum and shows how the fuzzy con-trol methodology can be used to construct a hybrid fuzzy control system. The hybridapproach incorporates proportional derivative control into a Takagi–Sugeno fuzzycontrol structure for stabilizing the inverted pendulum via a vertical force. Simu-lation results show the efficiency and accuracy of the proposed control method.

The fifth article, “A Direct Adaptive Neural Control Scheme with IntegralTerms” by Ieroham S. Baruch and Ruben Garrido, proposes a new direct adaptiveneural control scheme with single and double integral-plus-state actions. The con-trol scheme contains two recurrent trainable neural network models, which are aplant parameter identifier and state estimator. The good performance of the adap-tive control scheme is confirmed by closed-loop systems analysis and by simula-tion results of a multi-input multi-output plant with noise.

The sixth article, “Toward a Framework for the Specification of Hybrid FuzzyModeling” by Mercedes Valdés et al., describes a new approach for the specifica-tion of hybrid fuzzy models. This article shows the design of a new data-drivenfuzzy modeling framework, offering the functionalities needed to combine tech-niques into hybrid strategies for data-driven fuzzy modeling tasks. The proposedframework is also illustrated with interesting examples of hybridization.

The seventh article, “A Scalable Fuzzy Interference Canceler for DS-CDMASystems” by Joan Bas and Ana Pérez Neira, proposes a new interference cancelerbased on fuzzy logic that allows suppressing both analog and digital interferences,and easily controling the complexity versus performance trade-off. In other words,the design of the fuzzy canceler follows a down/top strategy; that is, first a designis made based on the linguistic information and, in a second stage, we refine the

128 CASTILLO AND MELIN

design by incorporating the available statistical information. The proposed fuzzycanceler is verified by finding its asymptotic performance bounds.

The eighth article, “Face Recognition Using Modular Neural Networks andthe Fuzzy Sugeno Integral for Response Integration” by Patricia Melin et al.,describes a new approach for face recognition using modular neural networks witha fuzzy logic method for response integration. The new architecture of modularneural networks is used for pattern recognition in the particular case of humanfaces. The fuzzy method for response integration is required to combine the out-puts of all the modules in the modular neural network. The new approach wasapplied for face recognition with a real database of faces from students of a par-ticular institution. Recognition rates with the modular approach were comparedagainst the monolithic single neural network approach, with excellent results.

The last article, “Black Box Evolutionary Mathematical Modeling Applied toLinear Systems” by Oscar Montiel et al., deals with the application of evolution-ary algorithms to the problem of mathematical modeling of linear systems. In par-ticular, the breeder genetic algorithm is analyzed in its explorative behavior forthe most common linear model structures. The breeder genetic algorithm uses fuzzyrecombination and the discrete mutation operator. Simulation results for the finiteimpulse response filter are shown to illustrate the applicability of the evolutionaryapproach in finding the best parameters of the filter.

We can conclude this introduction by saying that, in our opinion, all of thearticles in this special issue make an important contribution to the state of the artin the field of hybrid intelligent systems, in general, and also to the areas of fuzzylogic, neural networks, and genetic algorithms, in particular. Finally, we have tosay that it is our sincere hope that this issue will be of great interest and use toresearchers and students all over the world.

PREFACE 129