Showing papers on "Soft computing published in 1998"

Some reflections on soft computing, granular computing and their roles in the conception, design and utilization of information/intelligent systems

Abstract: We are living in a world which is undergoing profound changes brought about by rapid advances in science and technology. Among such changes, the most visible are those that relate to what is popularly referred to as the information revolution. The artifacts of this revolution are all around us: the e-mail, the world wide web, the cellular phone; the fax; and the desktop computer, among many others. Linked to the information revolution is another revolution — the intelligent systems revolution. The manifestations of this revolution are not as obvious as those of the information revolution because they involve, for the most part, not new products but higher MIQ (Machine IQ) of existing systems, products and devices. Among the familiar examples are smart appliances, smart cameras, smart robots and smart software for browsing, diagnosis, fraud detection and quality control. The information and intelligent systems revolutions are in a symbiotic relationship. Intelligence requires information and vice-versa. The confluence of intelligent systems and information systems leads to intelligent information systems. In this sense, the union of information systems, intelligent systems and intelligent information systems constitutes what might be referred to as information/intelligent systems, or I/IS for short. In my perception, in coming years, the design, construction and utilization of information/intelligent systems will become the primary focus of science and technology, and I/IS systems will become a dominant presence in our daily lives. When we take a closer look at information/intelligent systems what we see is the increasingly important role of soft computing (SC) in their conception, design and utilization. Basically, soft computing is an association of computing methodologies which includes as its principal members fuzzy

Advances in Soft Computing: Engineering Design and Manufacturing

Abstract: 1: Keynote Papers.- The NIST Design Repository Project.- Evolving Connectionist and Fuzzy-Connectionist Systems for On-line Adaptive Decision Making and Control.- Recent New Development in Evolutionary Programming.- Emotional Image Retrieval with Interactive Evolutionary Computation.- 2: Design Support Systems.- Using Genetic Algorithms to Encourage Engineering Design Creativity.- Abduction Problem in Probabilistic Constraint Logic Programming.- Aspects of Evolutionary Design by Computers.- Surface Optimisation within the CAD/CAM Environment using Genetic Algorithms.- 3: Intelligent Control.- Adaptive Sugeno Fuzzy Control: A Case Study.- An Experimental and Comparative Study of Fuzzy PID Control Structures.- An Accurate COG Defuzzifier Design Using the Coadaptation of Learning and Evolution.- A Multiagent Intelligent Control System for Glass Industry.- Predictive Control Using Fuzzy Models.- Evolutionary Design of a Helicopter Autopilot.- Decomposition of a Fuzzy Controller Based on Inference Break-up Method.- 4: Identification and Modelling.- Experimental Evaluation of Intelligent Identification Algorithms Applied to a Wind Tunnel Process.- Improvement of Membership Function Identification Method in Usability and Precision.- General Parameter Radial Basis Function Neural Network Based Adaptive Fuzzy Systems.- Uneven Division of Input Spaces for Hierarchical Fuzzy Modeling.- Ensembles of Evolutionary created Artificial Neural Networks and Nearest Neighbour Classifiers.- 5: Data Mining.- Application of Multi-dimensional Fuzzy Analysis to Decision Making.- Information-Theoretic Fuzzy Approach to Knowledge Discovery in Databases.- Intelligent Electronic Catalogs for Sales Support - Introducing Case-Based Reasoning Techniques to on-line Product Selection Applications.- A Genetic Algorithm for Generalized Rule Induction.- 6: Optimisation.- Multiobjective Optimization by Nessy Algorithm.- The Scout Algorithm applied to the Maximum Clique Problem.- Unconstrained Optimization Using Genetic Box Search.- Improvement of Simple Genetic Algorithm for Solving the Uncapacitated Warehouse Location Problem.- Optimizing Neural Networks for Time Series Prediction.- 7: Optimisation for Industrial Applications.- Maximum Entropy Image Restoration by Evolutionary Algorithm.- The Finite Element Method and Soft Computing.- A Tabu Search Approach for the Tool Assignment and Machine Loading Problem in Flexible Manufacturing Systems.- Investigating Evolutionary Optimisation of Constrained Functions to Capture Shape Descriptions from Range Data.- Optimal Selection of Pressure Vessels.- 8: New Topics in EA Basics.- Simulation of Baldwin Effect and Dawkins Memes by Genetic Algorithm.- Approach to Structure Synthesis by Genetic Algorithms.- A Study of Altruism by Genetic Algorithm.- The Bivariate Marginal Distribution Algorithm.- 9: New Frontier for Soft Computing.- Granular Computing using Neighborhood Systems.- Toward Fuzziness in Natural Language Processing.- A New Approach to Acquisition of Comprehensible Fuzzy Rules.- Zero-Point Probability for Linear Source Separation.- Code Optimization for DNA Computing of Maximal Cliques.- 10: Summary of Tutorials.- On Line Tutorials on Evolutionary Computing.- Fuzzy Control Tutorial.- 11: Summary of Discussion.- 11: Summary of Discussion.- Keyword Index.- List of Reviewers.

A robust voice activity detector for wireless communications using soft computing

Abstract: Discontinuous transmission based on speech/pause detection represents a valid solution to improve the spectral efficiency of new generation wireless communication systems. In this context, robust voice activity detection (VAD) algorithms are required, as traditional solutions present a high misclassification rate in the presence of the background noise typical of mobile environments. This paper presents a voice detection algorithm which is robust to noisy environments, thanks to a new methodology adopted for the matching process. More specifically, the VAD proposed is based on a pattern recognition approach in which the matching phase is performed by a set of six fuzzy rules, trained by means of a new hybrid learning tool. A series of objective tests performed on a large speech database, varying the signal-to-noise ratio (SNR), the types of background noise, and the input signal level, showed that, as compared with the VAD standardized by ITU-T in Recommendation G.729 annex B, the fuzzy VAD, on average, achieves an improvement in reduction both of the activity factor of about 25% and of the clipping introduced of about 43%. Informal listening tests also confirm an improvement in the perceived speech quality.

Roles of Soft Computing and Fuzzy Logic in the Conception, Design and Deployment of Information/Intelligent Systems

Abstract: The essence of soft computing is that, unlike the traditional, hard computing, it is aimed at an accommodation with the pervasive imprecision of the real world. Thus, the guiding principle of soft computing is: ‘….exploit the tolerance for imprecision, uncertainty and partial truth to achieve tractability, robustness, low solution cost and better rapport with reality.’ In the final analysis, the role model for soft computing is the human mind.

Genetic algorithm in search and optimization: the technique and applications

Abstract: A genetic algorithm (GA) is a search and optimization method developed by mimicking the evolutionary principles and chromosomal processing in natural genetics. A GA begins its search with a random set of solutions usually coded in binary string structures. Every solution is assigned a Htness which is directly related to the objective function of the search and optimization problem. Thereafter, the population of solutions is modiiied to a new population by applying three operators similar to natural genetic operatorsfreproduction, crossover, and mutation. A GA works iteratively by successively applying these three operators in each generation till a termination criterion is satisiied. Over the past one decade, GAs have been successfully applied to a wide variety of problems, because of their simplicity, global perspective, and inherent parallel processing. In this paper, we outline the working principle of a GA by describing these three operators and by outlining an intuitive sketch of why the GA is a useful search algorithm. Thereafter, we apply a GA to solve a complex engineering design problem. Finally, we discuss how GAs can enhance the performance of other soft computing techniques-fuzzy logic and neural network techniques.

A new fuzzy-fractal-genetic method for automated mathematical modelling and simulation of robotic dynamic systems

Abstract: We describe a computer program for mathematical modelling and simulation (MMS) of robotic dynamic systems (RDS) using fuzzy logic techniques, genetic algorithms and fractal theory. The computer program combines soft computing (SC) techniques with mathematical methods and can be considered as an intelligent system for the domain of modelling and simulation of robotic systems. The computer program simulates the reasoning of a human expert in the process of developing the mathematical models of RDS. The program contains the knowledge of the human experts expressed as fuzzy rules (in the knowledge base) for MMS of RDS. The computer program uses efficiently the SC techniques and fractal theory for MMS of RDS.

Rough set elements

Abstract: W: L. Polkowski and A. Skowron, editors, Rough Sets in Knowledge Discovery 1. Methodology and Applications, volume 18 of Studies in Fuzziness and Soft Computing, pages 10-30. Springer-Verlag, Heidelberg, Germany, 1998

NEFCLASS-X : a soft computing tool to build readable fuzzy classifiers : Towards engineering intelligent systems

Abstract: Neuro-fuzzy classification systems offer a means of obtaining fuzzy classification rules by a learning algorithm. Although it is usually no problem to find a suitable fuzzy classifier by learning from data, it can, however, be hard to obtain a classifier that can be interpreted conveniently. There is usually a trade-off between accuracy and readability. This paper discusses NEFCLASS - a neuro-fuzzy approach for classification problems - and its implementation NEFCLASS-X. It is shown how a readable fuzzy classifier can be obtained by a learning process and how interactive strategies for pruning rules and variables from a trained classifier can enhance its interpretability.

Introduction: Hybrid intelligent adaptive systems

Abstract: This issue of International Journal of Intelligent Systems includes extended versions of selected papers from the 4th International Conference on Soft Computing, held in Iizuka, Japan, September 30]October 5, 1996. The topic of the special issue is ‘‘Hybrid Intelligent Adaptive Systems.’’ Research on hybrid systems is one of the key issues of developing intelligent systems and it can apply a wide range of tools, including artificial neural networks, fuzzy logic, knowledge-based systems, genetic algorithms, evolutionary computation, and chaos models. The papers in this issue have been carefully reviewed and modified to give the readers a comprehensive overview of theoretical aspects, design, and implementation issues of hybrid intelligent adaptive systems. In the first paper by Kasabov and Kozma, a general framework of developing hybrid, intelligent, and adaptive systems is given. This work develops multimodular, fuzzy neural network systems and applies it to phoneme-based speech recognition. The second paper by Miyata, Furuhashi, and Uchikawa proposes fuzzy abductive inference with degrees of manifestations. This method infers irredundant combinations of candidates with degrees of belief for the manifestations. It is also demonstrated that the results of the inference method are applicable to medical diagnosis and system fault detection. Cho adopts ideas of artificial life in his work to develop evolutionary neural networks. The introduced modular neural network can evole its structure autonomously using a structural genetic code. The effectiveness of the method is demonstrated on the example of handwritten digit recognition. Feuring and Lippe study theoretical aspects of fuzzy neural networks. They propose a training algorithm for fuzzy neural networks that satisfies a certain goodness criterion. The second part of the special issue contains articles related to time series analysis and systems control. Yamazaki, Kang, and Ochiai introduce a hierarchical neural network system for adaptive, intelligent control, based on the analogy with the human thinking process. The optimum parameter space of the neural network system is found by a self-controllable algorithm, which can lead to either equilibrium or to nonequilibrium, chaotic behavior. The results of this study are applied, e.g., to laser beam analysis, semiconductor design, and design of magnetic devices. The work by Kozma, Kasabov, Kim, and Cohen presents a chaotic neuro-fuzzy method for time series analysis and process control. The

Soft computing for the intelligent robust control of a robotic unicycle with a new physical measure for mechanical controllability

Abstract: The posture stability and driving control of a human-riding-type unicycle have been realized. The robot unicycle is considered as a biomechanical system using an internal world representation with a description of emotion, instinct and intuition mechanisms. We introduced intelligent control methods based on soft computing and confirmed that such an intelligent control and biological instinct as well as intuition together with a fuzzy inference is very important for emulating human behaviors or actions. Intuition and instinct mechanisms are considered as global and local search mechanisms of the optimal solution domains for an intelligent behavior and can be realized by genetic algorithms (GA) and fuzzy neural networks (FNN) accordingly. For the fitness function of the GA, a new physical measure as the minimum entropy production for a description of the intelligent behavior in a biological model is introduced. The calculation of robustness and controllability of the robot unicycle is presented. This paper provides a general measure to estimate the mechanical controllability qualitatively and quantitatively, even if any control scheme is applied. The measure can be computed using a Lyapunov function coupled with the thermodynamic entropy change. Interrelation between Lyapunov function (stability condition) and entropy production of motion (controllability condition) in an internal biomechanical model is a mathematical background for the design of soft computing algorithms for the intelligent control of the robotic unicycle. Fuzzy simulation and experimental results of a robust intelligent control motion for the robot unicycle are discussed. Robotic unicycle is a new Benchmark of non-linear mechatronics and intelligent smart control.

A generic architecture for hybrid intelligent systems

Abstract: The integration of different learning and adaptation techniques in one architecture, to overcome individual limitations and achieve synergetic effects through hybridization or fusion of these techniques, has in recent years contributed to a large number of new intelligent system designs. Most of these approaches, however, follow an ad hoc design methodology, further justified by success in certain application domains. Due to the lack of a common framework it remains often difficult to compare the various systems conceptually and evaluate their performance comparatively. In this paper we first aim at classifying state-of-the-art intelligent systems, which have evolved over the past decade in the soft computing community. We identify four categories, based on the systems, overall architecture: (1) single component systems, (2) fusion-based systems, (3) hierarchical systems, and (4) hybrid systems. We then introduce a unifying paradigm, derived from concepts well known in the AI and agent community, as conceptual framework to better understand, modularize, compare and evaluate the individual approaches. We think it is crucial for the design of intelligent systems to focus on the integration and interaction of different learning techniques in one model rather then merging them to create ever new techniques. Two original instantiations of this framework are presented and discussed. Their performance is evaluated for prefetching of bulk data over wireless media.

Soft Computing and Hybrid AI Approaches to Intelligent Manufacturing

Abstract: The application of pattern recognition (PR) techniques, artificial neural networks (ANNs), and nowadays hybrid artificial intelligence (Al) techniques in manufacturing can be regarded as consecutive elements of a process started two decades ago. The fundamental aim of the paper is to outline the importance of soft computing and hybrid AI techniques in manufacturing by introducing a genetic algorithm (GA) based dynamic job shop scheduler and the integrated use of neural, fuzzy and GA techniques for modeling, control and monitoring purposes.

Logic Programming and Soft Computing

Abstract: From the Publisher: The challenge now facing AI is to produce systems exhibiting higher 'machine IQ' and using 'common sense' reasoning, rather than purely logical deduction. Soft computing is an attempt to meet this challenge, allowing computers to handle imprecision, uncertainty and partial truth. It has evolved from the success of fuzzy control and other area of 'sub-symbolic' artificial intelligence such as neural nets. This book presents different perspectives on how to combine soft computing's tolerance of imprecision with logic programming's power and semantics to provide a way forward for the implementation of intelligent knowledge-based systems.

Soft set approach to the subjective assessment of sound quality

Abstract: Subjective assessment of sound quality is an important issue in acoustics, telecommunications and multimedia. An attempt to assess sound quality based on the soft set approach was made. For that purpose techniques derived from rough and fuzzy set theories have been implemented. A short discussion of standard testing methods in acoustic sound quality evaluation was included Some exemplary data derived from subjective testing were presented and then processed using non-statistical (soft computing) methods. Conclusions resulting from the comparison of investigated approaches to the processing of subjective testing results were included.

Soft computing, fuzzy logic and recognition technology

Abstract: Recognition systems of one kind or another have been around for a long time. But what we are beginning to see today are recognition systems that are capable of performing tasks that could not be done in the past. The quantum jump in the capabilities of today's recognition systems reflect three converging developments: (a) major advances in sensor technology; (b) major advances in sensor data processing technology; and (c) the use of soft computing techniques to infer a conclusion from observed data.

A new method for adaptive model-based neuro-fuzzy-fractal control of Non-Linear Dynamic plants : The case of biochemical reactors

Abstract: We describe in this paper a new method for adaptive model-based control of non-linear dynamic plants using Neural Networks, Fuzzy Logic and Fractal Theory. The new neuro-fuzzy-fractal method combines Soft Computing (SC) techniques with the concept of the fractal dimension for the domain of Non-Linear Dynamic Plant Control. The new method for adaptive model-based control has been implemented as a computer program to show that our neuro-fuzzy-fractal approach is a good alternative for controlling non-linear dynamic plants. We illustrate in this paper our new methodology with the case of controlling biochemical reactors in the food industry. For this case, we use mathematical models for the simulation of bacteria growth for several types of food. The goal of constructing these models is to capture the dynamics of bactena population in food, so as to have a way of controlling this dynamics for industrial purposes.

Soft computing with neural networks for engineering applications: Fundamental issues and adaptive approaches

Abstract: Engineering problems are inherently imprecision tolerant. Biologically inspired soft computing methods are emerging as ideal tools for constructing intelligent engineering systems which employ approximate reasoning and exhibit imprecision tolerance. They also offer built-in mechanisms for dealing with uncertainty. The fundamental issues associated with engineering applications of the emerging soft computing methods are discussed, with emphasis on neural networks. A formalism for neural network representation is presented and recent developments on adaptive modeling of neural networks, specifically nested adaptive neural networks for constitutive modeling are discussed.

A new fuzzy-genetic approach for the simulation and forecasting of international trade non-linear dynamics

Abstract: The authors describe non-linear mathematical models that can be used to study the dynamics of international trade. Mathematical models of international trade (IT), between three or more countries, can show very complicated dynamics in time (with the possible occurrence of chaotic behavior). The simulation of these models is critical in understanding the behavior of the relevant financial and economical variables for the problem of IT. Also, performing the simulations for different parameter values of the models will enable the forecasting of future IT. The problem of simulation and forecasting of IT has been solved by using soft computing (SC) techniques. An intelligent system for automated simulation of IT, combining fuzzy logic techniques and genetic algorithms, has been developed for the simulation and behavior identification of the mathematical models. The intelligent system uses a specific genetic algorithm to generate the best set of parameter values for performing numerical simulation of the dynamical system (of IT) and a fuzzy rule base for behavior identification. The importance of simulation and forecasting of IT can be measured if one considers that one of the goals for a specific country is to find the optimum benefit from its international trade with other countries.

Applications of soft computing in drug design

Abstract: This review examines the role of soft computing methods such as artificial neural networks (ANNs), genetic algorithms (GAs), fuzzy logic (FL), chaos, fractals and cellular automata (CA) and their hybrids in the field of drug design. They have been found to be useful in a wide variety of areas including quantitative structure-activity relationship (QSAR), quantitative structure-property relationship (QSPR), variable selection, conformation searching, receptor docking, pharmacophore development, molecular design, combinatorial libraries, surface phenomena, kinetics and complex system studies. Based upon the studies examined, the use of soft computing techniques is likely to grow significantly in the future.

Applications, case studies and software systems

Abstract: Z Pawlak: Foreword L Polkowski, A Skowron: Introducing the Book- Applications: S Greco, B Matarazzo, R Slowinski: Rough Approximation of a Preference Relation in a Pairwise Comparison Table K Krawiec, R Slowinski, D Vanderpooten: Learning Decision Rules form Similiarity Based Rough Approximations S Hoa Nguyen, A Skowron, P Synak: Discovery of Data Patterns with Applications to Decomposition and Classification Problems ZW Ras: Answering Non-Standard Queries in Distributed Knowledge-Based Systems J Stepaniuk: Approximation Spaces, Reducts and Representatives N Zhong, JZ Dong, S Ohsuga: Data Mining: A Probabilistic Rough Set Approach- Case Studies: A Czyzewski: Soft Processing of Audio Signals K Furuta, M Hirokane, Y Mikumo: Extraction Method Based on Rough Set Theory of Rule-Type Knowledge from Diagnostic Cases of Slope-Failure Danger Levels B Kostek: Soft Computing-Based Recognition of Musical Sounds A Mrozel, K Skabek: Rough Sets in Economic Applixations K Slowinski, J Stefanowski: Multistage Rough Set Analysis of Therapeutic Experience with Acute Pancreatitis H Tanaka, Y Maeda: Reduction Methods for Medical Data S Tsumoto: Formalization and Induction of Medical Expert System Rules Based on Rough Set Theory D Van den Poel: Rough Sets for Database Marketing H Zang, R Swiniarski: A New Halftoning Method Based on Error Diffusion with Rough Set Filtering- Hybrid Approaches: C Browne, I Duntsch, G gediga: IRIS Revisited: A Comparison of Discriminant and Enhanced Rough Set Data Analysis R Lingras: Applications of Rough Patterns JF Peters III: Time and Clock Information Systems: Concepts and Roughly Fuzzy Petri Net Models Z Suraj: The Synthesis Problem of ConcurrentSystems Specified by Dynamic Information Systems MS Szczuka: Rough Sets and Artificial Neural Networks J Wroblewski: Genetic Algorithms in Decomposition and Classification Problems- Appendix 1: Rough Set Bibliography- Appendix 2: Software Systems

Human-centred Intelligent Systems and Soft Computing

Abstract: This paper considers the abstract features of human/machine interaction systems that are required for the production of intelligent behaviour. A conceptual architecture is then proposed for a subset of intelligent systems called human-centred intelligent systems (HCISs) and it is argued that such systems must be autonomous, robust and adaptive in order to be intelligent. Soft computing is proposed as a promising new technique that can be used to build HCISs, and examples are presented where this is already being done. Finally, flexibility is defined to be a combination of the often-conflicting requirements of robustness and adaptability, and it is argued that the right balance between these two features is necessary to achieve intelligent behaviour.

Progress in connectionist-based information systems: Proceedings of the 1997 International Conference on Neural Information Processing and Intelligent Information Systems

Abstract: These two volumes consist of about 350 papers in three main areas of artificial intelligence and neurocomputing, namely: (1) modelling the brain; (2) methods of soft computing; (3) applications of intelligent information systems. The materials, contained in two volumes, emphasise the importance of connectionist-based information systems which use neural networks and other methods to achieve intelligent information processing, such as speech recognition and language understanding, pattern recognition, vision, learning and adaptation, planning, and decision making. Some of the methods of the connectionist-based information systems directly model the physical organisation of the human brain, which is the area of brain-like computing. Other methods model cognitive aspects of human behaviours, which is the area of cognitive engineering. A third group of methods are based on statistical and probability theory. All these methods are presented and applied on concrete problems. Many connectionist-based systems are described in different papers of the two volumes. These two volumes are a comprehensive and up-to-date guide to the diverse topics of neuro-computing, artificial intelligence and knowledge engineering.

Soft Computing-Based Recognition of Musical Sounds

Abstract: Due to the development of multimedia technology and digital transmission of signals, there is rapid growth in the amount of audio data stored on various computer sites. Consequently, the problem is to find methods allowing one to explore a huge collection of data in order to find needed information in an effective way.

A pattern classification proposal for object-oriented audio coding in MPEG-4

Abstract: The future MPEG?4 standard will adopt an object?oriented encoding strategy whereby an audio source is encoded at a very low bit?rate by adapting a suitable coding scheme to the local characteristics of the signal. One of the most delicate issues in this approach is that the overall performance of the audio encoder greatly depends on the accuracy with which the input signal is classified. This paper shows that the difficult problem of audio classification for object?oriented coding can be effectively solved by selecting a salient set of acoustic parameters and adopting a fuzzy model for each audio object, obtained by a soft computing?hybrid learning tool. The audio classifier proposed operates at two levels: recognition of the class to which the input signal belongs (talkspurt, music, noise, signaling tones) and then recognition of the subclass to which it belongs. The results obtained show that fuzzy logic is a valid alternative to the matching techniques of a traditional pattern recognition approach.

Applications of soft computing to motion control

Abstract: Soft computing approaches have been attracting interest. Some application studies of soft computing approach to motor/motion control, although they are in an early stage, are described based on the simulation and experimental studies developed at Matsui Laboratory, Nagoya Institute of Technology, Japan. Fuzzy inference-based online gain tuning of a servo drive system is considered. The proposed system has, in addition to a position feedforward loop, conventional P position and PI speed compensators, thus constituting a two-degrees-of-freedom position controller.

Soft Processing of Audio Signals

Abstract: The aim of the presented research is to develop and to test some digital signal processing systems applicable to modern telecommunications. A special feature of the elaborated systems is the improvement in performance of audio signal processing algorithms obtained through the use of some soft computing methods based on rough sets, fuzzy logic and neural networks. The engineered and tested digital signal processing systems enabled some comparative studies of the effectiveness of algorithms based on soft computing. The results of speaker-independent recognition of digits and of noise removal from speech and music signals will be presented. Some general conclusions concerning the application of intelligent decision systems to real-time signal processing will be added.