Academic literature on the topic 'Soft computing. Computer networks Fuzzy systems. Probabilities'

We are witnessing a rapidly growing interest in the field of advanced computational intelligence, a "soft computing" technique. As Prof. Zadeh has stated, soft computing integrates fuzzy logic, neural networks, evolutionary computation, and chaos. Soft computing is the most important technology available for designing intelligent systems and control. The difficulties of fuzzy logic involve acquiring knowledge from experts and finding knowledge for unknown tasks. This is related to design problems in constructing fuzzy rules. Neural networks and genetic algorithms are attracting attention for their potential in raising the efficiency of knowledge finding and acquisition. Combining the technologies of fuzzy logic and neural networks and genetic algorithms, i.e., soft computing techniques will have a tremendous impact on the fields of intelligent systems and control design. To explain the apparent success of soft computing, we must determine the basic capabilities of different soft computing frameworks. Give the great amount of research being done in these fields, this issue addresses fundamental capabilities. This special issue is devoted to advancing computational intelligence in control theory and applications. It contains nine excellent papers dealing with advanced computational intelligence in control theory and applications such as fuzzy control and stability, mobile robot control, neural networks, gymnastic bar action, petroleum plant control, genetic programming, Petri net, and modeling and prediction of complex systems. As editor of this special issue, I believe that the excellent research results it contains provide the basis for leadership in coming research on advanced computational intelligence in control theory and applications.

Abstract: Neuro Fuzzy is a hybrid system that combines Artificial Neural Networks with Fuzzy Logic. Provides a great deal of freedom when it comes to thinking. This phrase, on the other hand, is frequently used to describe a system that combines both approaches. There are two basic streams of neural network and fuzzy system study. Modelling several elements of the human brain (structure, reasoning, learning, perception, and so on) as well as artificial systems and data: pattern clustering and recognition, function approximation, system parameter estimate, and so on. In general, neural networks and fuzzy logic systems are parameterized nonlinear computing methods for numerical data processing (signals, images, stimuli). These algorithms can be integrated into dedicated hardware or implemented on a general-purpose computer. The network system acquires knowledge through a learning process. Internal parameters are used to store the learned information (weights). Keywords: Artificial Neural Networks (ANNs), Neural Networks (NNs), Fuzzy Logic (FL), Neuro-Fuzzy, Probability Reasoning, Soft Computing, Fuzzification, Defuzzification, Fuzzy Inference Systems, Membership Function.

The paper deals with the application of fuzzy systems, artificial neural networks (neural systems), and genetic algorithms to solve modeling and control problems in system engineering. Part 1 the paper covers the design of classical PID and fuzzy PID controllers for nonlinear systems with an (approximately) known dynamic model. Optimal controllers are designed based on genetic algorithms. Part 2 considers neural control of a SCARA robot. Part 3 deals with the fuzzy control of a special class of MIMO nonlinear systems and generalizes the method of Wang for such systems.

In this paper, we conduct in-depth research and analysis on the intelligent recognition and teaching of English fuzzy text through parallel projection and region expansion. Multisense Soft Cluster Vector (MSCVec), a multisense word vector model based on nonnegative matrix decomposition and sparse soft clustering, is constructed. The MSCVec model is a monolingual word vector model, which uses nonnegative matrix decomposition of positive point mutual information between words and contexts to extract low-rank expressions of mixed semantics of multisense words and then uses sparse. It uses the nonnegative matrix decomposition of the positive pointwise mutual information between words and contexts to extract the low-rank expressions of the mixed semantics of the polysemous words and then uses the sparse soft clustering algorithm to partition the multiple word senses of the polysemous words and also obtains the global sense of the polysemous word affiliation distribution; the specific polysemous word cluster classes are determined based on the negative mean log-likelihood of the global affiliation between the contextual semantics and the polysemous words, and finally, the polysemous word vectors are learned using the Fast text model under the extended dictionary word set. The advantage of the MSCVec model is that it is an unsupervised learning process without any knowledge base, and the substring representation in the model ensures the generation of unregistered word vectors; in addition, the global affiliation of the MSCVec model can also expect polysemantic word vectors to single word vectors. Compared with the traditional static word vectors, MSCVec shows excellent results in both word similarity and downstream text classification task experiments. The two sets of features are then fused and extended into new semantic features, and similarity classification experiments and stack generalization experiments are designed for comparison. In the cross-lingual sentence-level similarity detection task, SCLVec cross-lingual word vector lexical-level features outperform MSCVec multisense word vector features as the input embedding layer; deep semantic sentence-level features trained by twin recurrent neural networks outperform the semantic features of twin convolutional neural networks; extensions of traditional statistical features can effectively improve cross-lingual similarity detection performance, especially cross-lingual topic model (BL-LDA); the stack generalization integration approach maximizes the error rate of the underlying classifier and improves the detection accuracy.

This special issue features five papers devoted to fuzzy systems and their applications. Papers were selected from those accepted and presented at the Sixth International Conference on Fuzzy Systems (AFSS' 2004) held in Hanoi, Vietnam on December 15-17, 2004. AFSS' 2004 and Tutorials held in Hue city on December 18-19, 2004, included a wide spectrum of research topics on "fuzzy set theory", "intelligent technology", "fuzzy logic and approximate reasoning", "neural networks", "genetic algorithms", "hybrid systems" and "soft computing". Over 40 papers were accepted and presented by researchers from countries including Brazil, Canada, Taiwan, India, Korea, Malaysia, Japan and Vietnam. Five papers receiving outstanding recommendations in reviews have been selected for this issue. The topics they address include fuzzy logic for robots, data mining, neural networks in medicine, Fuzzy Constraint Satisfaction Problems, and hybrid systems. As editors of this special issue, we are sincerely grateful to the authors. Special thanks also go to the referees for their excellent work, to Mr. Kazuki Ohmori for his aid in coordinating the issue's publication, and to the JACIII Editorial Board, especially Professor Kaoru Hirota for his invaluable support and encouragement. Finally, we thank Professors Masao Mukaidono and Witold Pedrycz for their contributions to AFSS' 2004. Without their support, AFSS' 2004 and this issue would not have been possible.

The "information revolution" of our time affects our entire generation. While a vision of the "Information Society," with its financial, legal, business, privacy, and other aspects has emerged in the past few years, the "traditional scene" of information technology, that is, industrial automation, maintained its significance as a field of unceasing development. Since the old-fashioned concept of "Hard Automation" applicable only to industrial processes of fixed, repetitive nature and manufacturing large batches of the same product1)was thrust to the background by keen market competition, the key element of this development remained the improvement of "Machine Intelligence". In spite of the fact that L. A. Zadeh already introduced the concept of "Machine Intelligence Quotient" in 1996 to measure machine intelligence2) , this term remained more or less of a mysterious meaning best explicable on the basis of practical needs. The weak point of hard automation is that the system configuration and operations are fixed and cannot be changed without incurring considerable cost and downtime. Mainly it can be used in applications that call for fast and accurate operation in large batch production. Whenever a variety of products must be manufactured in small batches and consequently the work-cells of a production line should be quickly reconfigured to accommodate a change in products, hard automation becomes inefficient and fails due to economic reasons. In these cases, new, more flexible way of automation, so-called "Soft Automation," are expedient and suitable. The most important "ingredient" of soft automation is its adaptive ability for efficiently coping with changing, unexpected or previously unknown conditions, and working with a high degree of uncertainty and imprecision since in practice increasing precision can be very costly. This adaptation must be realized without or within limited human interference: this is one essential component of machine intelligence. Another important factor is that engineering practice often must deal with complex systems of multiple variable and multiple parameter models almost always with strong nonlinear coupling. Conventional analysis-based approaches for describing and predicting the behavior of such systems in many cases are doomed to failure from the outset, even in the phase of the construction of a more or less appropriate mathematical model. These approaches normally are too categorical in the sense that in the name of "modeling accuracy," they try to describe all structural details of the real physical system to be modeled. This significantly increases the intricacy of the model and may result in huge computational burden without considerably improving precision. The best paradigm exemplifying this situation may be the classic perturbation theory: the less significant the achievable correction is, the more work must be invested for obtaining it. Another important component of machine intelligence is a kind of "structural uniformity" giving room and possibility to model arbitrary particular details a priori not specified and unknown. This idea is similar to that of the ready-to-wear industry, whose products can later be slightly modified in contrast to the custom-tailors' made-to-measure creations aiming at maximum accuracy from the beginning. Machines carry out these later corrections automatically. This "learning ability" is another key element of machine intelligence. To realize the above philosophy in a mathematically correct way, L. A. Zadeh separated Hard Computing from Soft Computing. This revelation immediately resulted in distinguishing between two essential complementary branches of machine intelligence: Hard Computing based Artificial Intelligence and Soft Computing based Computational Intelligence. In the last decades, it became generally known that fuzzy logic, artificial neural networks, and probabilistic reasoning based Soft Computing is a fruitful orientation in designing intelligent systems. Moreover, it became generally accepted that soft computing rather than hard computing should be viewed as the foundation of real machine intelligence via exploiting the tolerance for imprecision, uncertainty and partial truth to achieve tractability, robustness, low solution cost and better rapport with reality. Further research in the past decade confirmed the view that typical components of present soft computing such as fuzzy logic, neurocomputing, evolutionary computation and probabilistic reasoning are complementary and best results can be obtained by their combined application. These complementary branches of Machine Intelligence, Artificial Intelligence and Computational Intelligence, serve as the basis of Intelligent Engineering Systems. The huge number of scientific results published in journals and conference proceedings worldwide substantiates this statement. Three years ago, a new series of conferences in this direction was initiated and launched with the support of several organizations including the IEEE Industrial Electronics Society and IEEE Hungary Section in technical cooperation with IEEE Robotics & Automation Society. The first event of the series hosted by Bdnki Dondt Polytechnic, Budapest, Hungary, was called "19997 IEEE International Conference on Intelligent Engineering Systems " (INES'97). The Technical University of Vienna, Austria hosted the next event of the series in 1998, followed by INES'99 held by the Technical University of Kosice, Slovakia. The present special issue consists of the extended and revised version of the most interesting papers selected out of the presentations of this conference. The papers exemplify recent development trends of intelligent engineering systems. The first paper pertains to the wider class of neural network applications. It is an interesting report of applying a special Adaptive Resonance Theory network for identifying objects in multispectral images. It is called "Extended Gaussian ARTMAP". The authors conclude that this network is especially advantageous for classification of large, low dimensional data sets. The second paper's subject belongs to the realm of fuzzy systems. It reports successful application of fundamental similarity relations in diagnostic systems. As an example failure detection of rolling-mill transmission is considered. The next paper represents the AI-branch of machine intelligence. The paper is a report on an EU-funded project focusing on the storage of knowledge in a corporate organizational memory used for storing and retrieving knowledge chunks for it. The flexible structure of the system makes it possible to adopt it to different SMEs via using company-specific conceptual terms rather than traditional keywords. The fourth selected paper's contribution is to the field of knowledge discovery. For this purpose in the first step, cluster analysis is done. The method is found to be helpful whenever little or no information on the characteristics of a given data set is available. The next paper approaches scheduling problems by the application of the multiagent system. It is concluded that due to the great number of interactions between components, MAS seems to be well suited for manufacturing scheduling problems. The sixth selected paper's topic is emerging intelligent technologies in computer-aided engineering. It discusses key issues of CAD/CAM technology of our days. The conclusion is that further development of CAD/CAM methods probably will serve companies on the competitive edge. The seventh paper of the selection is a report on seeking a special tradeoff between classical analytical modeling and traditional soft computing. It nonconventionally integrates uniform structures obtained from Lagrangian Classical Mechanics with other simple elements of machine intelligence such as saturated sigmoid transition functions borrowed from neural nets, and fuzzy rules with classical PID/ST, and a simplified version of regression analysis. It is concluded that these different components can successfully cooperate in adaptive robot control. The last paper focuses on the complexity problem of fuzzy and neural network approaches. A fuzzy rule base, be it generated from expert operators or by some learning or identification schemes, may contain redundant, weakly contributing, or outright inconsistent components. Moreover, in pursuit of good approximation, one may be tempted to overly assign the number of antecedent sets, thereby resulting in large fuzzy rule bases and much problems in computation time and storage space. Engineers using neural networks have to face the same complexity problem with the number of neurons and layers. A fuzzy rule base and neural network design, hence, have two important objectives. One is to achieve a good approximation. The other is to reduce the complexity. The main difficulty is that these two objectives are contradictory. A formal approach to extracting the more pertinent elements of a given rule set or neurons is, hence, highly desirable. The last paper is an attempt in this direction. References 1)C. W. De Silva. Automation Intelligence. Engineering Application of Artificial Intelligence. Vol. 7. No. 5. 471-477 (1994). 2)L. A. Zadeh. Fuzzy Logic, Neural Networks and Soft Computing. NATO Advanced Studies Institute on Soft Computing and Its Application. Antalya, Turkey. (1996). 3)L. A. Zadeh. Berkeley Initiative in Soft Computing. IEEE Industrial Electronics Society Newsletter. 41, (3), 8-10 (1994).

Welcome to this special issue of the Journal of Advanced Computational Intelligence and Intelligent Informatics (JACIII). I am pleased to introduce 41 selected papers presented at the 3rd International Conference on Soft Computing and Intelligent Systems (SCIS) and the 7th International Symposium on Advanced Intelligent Systems (ISIS) held on September 17-21, 2008, at Nagoya University in Nagoya, Japan. This conference featured 401 original papers in presentations attended by some 500 participants. SCIS & ISIS is a biennial international joint conference in the field of soft computing and intelligent systems, including branches of research ranging from fuzzy systems, neural networks, and evolutionary computation to multiagent systems, artificial intelligence, and robotics. This current issue presents 20 papers covering most of the conference topics including fuzzy theory, self-organizing maps, robotics, computer vision, and optimization algorithms. I would like to thank the authors and reviewers and SCIS & ISIS 2008 for making this special issue possible. I am also grateful to Prof. Toshio Fukuda, Nagoya University, and Prof. Kaoru Hirota, Tokyo Institute of Technology, the editors-in-chief, and the SCIS & ISIS 2008 conference staff for inviting me to guest-edit this Journal.

Building intelligent systems has been one of the great challenges since the early days of human culture. From the second half of the 18th century, two revolutionary changes played the key role in technical development, hence in creating engineering and intelligent engineering systems. The industrial revolution was made possible through technical advances, and muscle power was replaced by machine power. The information revolution of our time, in turn, canbe characterized as the replacement of brain power by machine intelligence. The technique used to build engineering systems and replace muscle power can be termed "Hard Automation"1) and deals with industrial processes that are fixed and repetitive in nature. In hard automation, the system configuration and the operations are fixed and cannot be changed without considerable down-time and cost. It can be used, however, particularly in applications calling for fast, accurate operation, when manufacturing large batches of the same product. The "intelligent" area of automation is "Soft Automation," which involves the flexible, intelligent operation of an automated process. In flexible automation, the task is programmable and a work cell must be reconfigured quickly to accommodate a product change. It is particularly suitable for plant environments in which a variety of products is manufactured in small batches. Processes in flexible automation may have unexpected or previously unknown conditions, and would require a certain degree of "machine" intelligence to handle them.The term machine intelligence has been changing with time and is machinespecific, so intelligence in this context still remains more or less a mysterious phenomenon. Following Prof. Lotfi A. Zadeh,2) we consider a system intelligent if it has a high machine intelligence quotient (MIQ). As Prof. Zadeh stated, "MIQ is a measure of intelligence of man-made systems," and can be characterized by its well defined dimensions, such as planning, decision making, problem solving, learning reasoning, natural language understanding, speech recognition, handwriting recognition, pattern recognition, diagnostics, and execution of high level instructions.Engineering practice often involves complex systems having multiple variable and multiple parameter models, sometimes with nonlinear coupling. The conventional approaches for understanding and predicting the behavior of such systems based on analytical techniques can prove to be inadequate, even at the initial stages of setting up an appropriate mathematical model. The computational environment used in such an analytical approach is sometimes too categoric and inflexible in order to cope with the intricacy and complexity of real-world industrial systems. It turns out that, in dealing with such systems, one must face a high degree of uncertainty and tolerate great imprecision. Trying to increase precision can be very costly.In the face of the difficulties above, Prof. Zadeh proposes a different approach for Machine Intelligence. He separates Hard Computing techniques based Artificial Intelligence from Soft Computing techniques based Computational Intelligence.•Hard computing is oriented toward the analysis and design of physical processes and systems, and is characterized by precision, formality, and categorization. It is based on binary logic, crisp systems, numerical analysis, probability theory, differential equations, functional analysis, mathematical programming approximation theory, and crisp software.•Soft computing is oriented toward the analysis and design of intelligent systems. It is based on fuzzy logic, artificial neural networks, and probabilistic reasoning, including genetic algorithms, chaos theory, and parts of machine learning, and is characterized by approximation and dispositionality.In hard computing, imprecision and uncertainty are undesirable properties. In soft computing, the tolerance for imprecision and uncertainty is exploited to achieve an acceptable solution at low cost, tractability, and a high MIQ. Prof. Zadeh argues that soft rather than hard computing should be viewed as the foundation of real machine intelligence. A center has been established - the Berkeley Initiative for Soft Computing (BISC) - and he directs it at the University of California, Berkeley. BISC devotes its activities to this concept.3) Soft computing, as he explains2),•is a consortium of methodologies providing a foundation for the conception and design of intelligent systems,•is aimed at formalizing of the remarkable human ability to make rational decision in an uncertain, imprecise environment.The guiding principle of soft computing, given by Prof. Zadeh2) is: Exploit the tolerance for imprecision, uncertainty, and partial truth to achieve tractability, robustness, low solution cost, and better rapport with reality.Fuzzy logic is mainly concerned with imprecision and approximate reasoning, neurocomputing mainly with learning and curve fitting, genetic computation mainly with searching and optimization and probabilistic reasoning mainly with uncertainty and propagation of belief. The constituents of soft computing are complementary rather than competitive. Experience gained over the past decade indicates that it can be more effective to use them combined, rather than exclusively.Based on this approach, machine intelligence, including artificial intelligence and computational intelligence (soft computing techniques) is one pillar of Intelligent Engineering Systems. Hundreds of new results in this area are published in journals and international conference proceedings. One such conference, organized in Budapest, Hungary, on September 15-17, 1997, was titled'IEEE International Conference on Intelligent Engineering Systems 1997' (INES'97), sponsored by the IEEE Industrial Electronics Society, IEEE Hungary Section, Bá{a}nki Doná{a}t Polytechnic, Hungary, National Committee for Technological Development, Hungary, and in technical cooperation with the IEEE Robotics & Automation Society. It had around 100 participants from 29 countries. This special issue features papers selected from those papers presented during the conference. It should be pointed out that these papers are revised and expanded versions of those presented.The first paper discusses an intelligent control system of an automated guided vehicle used in container terminals. Container terminals, as the center of cargo transportation, play a key role in everyday cargo handling. Learning control has been applied to maintaining the vehicle's course and enabling it to stop at a designatedlocation. Speed control uses conventional control. System performance system was evaluated by simulation, and performance tests slated for a test vehicle.The second paper presents a real-time camera-based system designed for gaze tracking focused on human-computer communication. The objective was to equip computer systems with a tool that provides visual information about the user. The system detects the user's presence, then locates and tracks the face, nose and both eyes. Detection is enabled by combining image processing techniques and pattern recognition.The third paper discusses the application of soft computing techniques to solve modeling and control problems in system engineering. After the design of classical PID and fuzzy PID controllers for nonlinear systems with an approximately known dynamic model, the neural control of a SCARA robot is considered. Fuzzy control is discussed for a special class of MIMO nonlinear systems and the method of Wang generalized for such systems.The next paper describes fuzzy and neural network algorithms for word frequency prediction in document filtering. The two techniques presented are compared and an alternative neural network algoritm discussed.The fifth paper highlights the theory of common-sense knowledge in representation and reasoning. A connectionist model is proposed for common-sense knowledge representation and reasoning, and experimental results using this method presented.The next paper introduces an expert consulting system that employs software agents to manage distributed knowledge sources. These individual software agents solve users' problems either by themselves or thorough mutual cooperation.The last paper presents a methodology for creating and applying a generic manufacturing process model for mechanical parts. Based on the product model and other up-to-date approaches, the proposed model involves all possible manufacturing process variants for a cluster of manufacturing tasks. The application involves a four-level model structure and Petri net representation of manufacturing process entities. Creation and evaluation of model entities and representation of the knowledge built in the shape and manufacturing process models are emphasised. The proposed process model is applied in manufacturing process planning and production scheduling.References:1) C. W. De Silva, "Automation Intelligence," Engineering Application of Artificial Intelligence, 7-5, 471-477, (1994).2) L. A. Zadeh, "Fuzzy Logic, Neural Networks and Soft Computing," NATO Advanced Studies Institute on Soft Computing and Its Application, Antalya, Turkey, (1996).3) L. A. Zadeh, "Berkeley Initiative_in Soft Computing," IEEE Industrial Electronics Society Newsletter. 41-3, 8-10, (1994).

We are very pleased and honored to have an opportunity to publish a new journal the "International Journal of Advanced Computational Intelligence" (JACI). The JACI is a new, bimonthly journal covering the field of computer science. This journal focuses on advanced computational intelligence, including the synergetic integration of neural networks, fuzzy logic and evolutionary computations, in order to assist in fostering the application of intelligent systems to industry. This new field is called computational intelligence or soft computing. It has already been studied by many researchers, but no single, integrated journal exists anywhere in the world. This new journal gives readers the state of art of the theory and application of Advanced Computational Intelligence. The Topics include, but are not limited to: Fuzzy Logic, Neural Networks, GA and Evolutionary Computation, Hybrid Systems, Network Systems, Multimedia, the Human Interface, Biologically-Inspired Evolutionary Systems, Artificial Life, Chaos, Fractal, Wavelet Analysis, Scientific Applications and Industrial Applications. The journal, JACI, is supported by many researchers and scientific organizations, e.g., the International Fuzzy Systems Association (IFSA), the Japan Society of Fuzzy Theory and Systems (SOFT), the Brazilian Society of Automatics (SBA) and The Society of Instrument and Control Engineers (SICE), and we are currently negotiating with the John von Neumann Computer Society (in Hungary). Our policy is to have world-wide communication with many societies and researchers in this field. We would appreciate it if those organizations and people who have an interest in co-sponsorship or have proposals for special issues in this journal, as well as paper submissions, could contact us. Finally our special thanks go to the editorial office of Fuji Technology Press Ltd., especially to its president, Mr. K. Hayashi, and to the editor, Mr. Y. Inoue, for their efforts in publishing this new journal. Lotti A. Zadeh The publication of the International Journal of Advanced Computational Intelligence (JACI) is an important milestone in the advancement of our understanding of how intelligent systems can be conceived, designed, built, and deployed. When one first hears of computational intelligence, a question that naturally arises is: What is the difference, if any, between computational intelligence (CI) and artificial intelligence (AI)? As one who has witnessed the births of both AI and CI, I should like to suggest an answer. As a branch of science and technology, artificial intelligence was born about four decades ago. From the outset, AI was based on classical logic and symbol manipulation. Numerical computations were not welcomed and probabilistic techniques were proscribed. Mainstream AI continued to evolve in this spirit, with symbol manipulation still occupying the center of the stage, but not to the degree that it did in the past. Today, probabilistic techniques and neurocomputing are not unwelcome, but the focus is on distributed intelligence, agents, man-machine interfaces, and networking. With the passage of time, it became increasing clear that symbol manipulation is quite limited in its ability to serve as a foundation for the design of intelligent systems, especially in the realms of robotics, computer vision, motion planning, speech recognition, handwriting recognition, fault diagnosis, planning, and related fields. The inability of mainstream AI to live up to expectations in these application areas has led in the mid-eighties to feelings of disenchantment and widespread questioning of the effectiveness of AI's armamentarium. It was at this point that the name computational intelligence was employed by Professor Nick Cercone of Simon Fraser University in British Columbia to start a new journal named Computational Intelligence -a journal that was, and still is, intended to provide a broader conceptual framework for the conception and design of intelligent systems than was provided by mainstream AI. Another important development took place. The concept of soft computing (SC) was introduced in 1990-91 to describe an association of computing methodologies centering on fuzzy logic (FL), neurocomputing (NC), genetic (or evolutionary) computing (GC), and probabilistic computing (PC). In essence, soft computing differs from traditional hard computing in that it is tolerant of imprecision, uncertainty and partial truth. The basic guiding principle of SC is: Exploit the tolerance for imprecision, uncertainty, and partial truth to achieve tractability, robustness, low solution cost, and better rapport with reality. More recently, the concept of computational intelligence had reemerged with a meaning that is substantially different from that which it had in the past. More specifically, in its new sense, CI, like AI, is concerned with the conception, design, and deployment of intelligent systems. However, unlike mainstream AI, CI methodology is based not on predicate logic and symbol manipulation but on the methodologies of soft computing and, more particularly, on fuzzy logic, neurocomputing, genetic(evolutionary) computing, and probabilistic computing. In this sense, computational intelligence and soft computing are closely linked but not identical. In basic ways, the importance of computational intelligence derives in large measure from the effectiveness of the techniques of fuzzy logic, neurocomputing, genetic (evolutionary) computing, and probabilistic computing in the conception and design of information/intelligent systems, as defined in the statements of the aims and scope of the new journal of Advanced Computational Intelligence. There is one important aspect of both computational intelligence and soft computing that should be stressed. The methodologies which lie at the center of CI and SC, namely, FL, NC, genetic (evolutionary) computing, and PC are for the most part complementary and synergistic, rather than competitive. Thus, in many applications, the effectiveness of FL, NC, GC, and PC can be enhanced by employing them in combination, rather than in isolation. Intelligent systems in which FL, NC, GC, and PC are used in combination are frequently referred to as hybrid intelligent systems. Such systems are likely to become the norm in the not distant future. The ubiquity of hybrid intelligent systems is likely to have a profound impact on the ways in which information/intelligent systems are conceived, designed, built, and interacted with. At this juncture, the most visible hybrid intelligent systems are so-called neurofuzzy systems, which are for the most part fuzzy-rule-based systems in which neural network techniques are employed for system identification, rule induction, and tuning. The concept of neurofuzzy systems was originated by Japanese scientists and engineers in the late eighties, and in recent years has found a wide variety of applications, especially in the realms of industrial control, consumer products, and financial engineering. Today, we are beginning to see a widening of the range of applications of computational intelligence centered on the use of neurofuzzy, fuzzy-genetic, neurogenetic, neurochaotic and neuro-fuzzy-genetic systems. The editors-in-chief of Advanced Computational Intelligence, Professors Fukuda and Hirota, have played and are continuing to play majors roles both nationally and internationally in the development of fuzzy logic, soft computing, and computational intelligence. They deserve our thanks and congratulations for conceiving the International Journal of Advanced Computational Intelligence and making it a reality. International in both spirit and practice, JACI is certain to make a major contribution in the years ahead to the advancement of the science and technology of man-made information/intelligence systems -- systems that are at the center of the information revolution, which is having a profound impact on the ways in which we live, communicate, and interact with the real world. Lotfi A. Zadeh Berkeley, CA, July 24, 1997

It has been shown by Roger Jang in his paper titled "Adaptive-network-based fuzzy inference systems" that the Adaptive Network based Fuzzy Inference System can model nonlinear functions, identify nonlinear components in a control system, and predict a chaotic time series. The system use hybrid-learning procedure which employs the back-propagation-type gradient descent algorithm and the least squares estimator to estimate parameters of the model. However the learning procedure has several shortcomings due to the fact that * There is a harmful and unforeseeable influence of the size of the partial derivative on the weight step in the back-propagation-type gradient descent algorithm. *In some cases the matrices in the least square estimator can be ill-conditioned. *Several estimators are known which dominate, or outperform, the least square estimator. Therefore this thesis develops a new system that overcomes the above problems, which is called the "Faster Adaptive Network Fuzzy Inference System" (FANFIS). The new system in this thesis is shown to significantly out perform the existing method in predicting a chaotic time series , modelling a three-input nonlinear function and identifying dynamical systems. We also use FANFIS to predict five major stock closing prices in New Zealand namely Air New Zealand "A" Ltd., Brierley Investments Ltd., Carter Holt Harvey Ltd., Lion Nathan Ltd. and Telecom Corporation of New Zealand Ltd. The result shows that the new system out performed other competing models and by using simple trading strategy, profitable forecasting is possible.

Thesis (Ph. D.)--State University of New York at Binghamton, Thomas J. Watson School of Engineering and Applied Science, Department of Mechanical Engineering, 2008.
Includes bibliographical references.

The military recently recognized the importance of taking sociocultural factors into consideration. Therefore, Human Social Culture Behavior (HSCB) modeling has been getting much attention in current and future operational requirements to successfully understand the effects of social and cultural factors on human behavior. There are different kinds of modeling approaches to the data that are being used in this field and so far none of them has been widely accepted. HSCB modeling needs the capability to represent complex, ill-defined, and imprecise concepts, and soft computing modeling can deal with these concepts. There is currently no study on the use of any computational methodology for representing the relationship between adverse events and infrastructure development investments in an active war theater. This study investigates the relationship between adverse events and infrastructure development projects in an active war theater using soft computing techniques including fuzzy inference systems (FIS), artificial neural networks (ANNs), and adaptive neuro-fuzzy inference systems (ANFIS) that directly benefits from their accuracy in prediction applications. Fourteen developmental and economic improvement project types were selected based on allocated budget values and a number of projects at different time periods, urban and rural population density, and total adverse event numbers at previous month selected as independent variables. A total of four outputs reflecting the adverse events in terms of the number of people killed, wounded, hijacked, and total number of adverse events has been estimated. For each model, the data was grouped for training and testing as follows: years between 2004 and 2009 (for training purpose) and year 2010 (for testing). Ninety-six different models were developed and investigated for Afghanistan and the country was divided into seven regions for analysis purposes. Performance of each model was investigated and compared to all other models with the calculated mean absolute error (MAE) values and the prediction accuracy within &"177;1 error range (difference between actual and predicted value). Furthermore, sensitivity analysis was performed to determine the effects of input values on dependent variables and to rank the top ten input parameters in order of importance. According to the the results obtained, it was concluded that the ANNs, FIS, and ANFIS are useful modeling techniques for predicting the number of adverse events based on historical development or economic projects' data. When the model accuracy was calculated based on the MAE for each of the models, the ANN had better predictive accuracy than FIS and ANFIS models in general as demonstrated by experimental results. The percentages of prediction accuracy with values found within ± error range around 90%. The sensitivity analysis results show that the importance of economic development projects varies based on the regions, population density, and occurrence of adverse events in Afghanistan. For the purpose of allocating resources and development of regions, the results can be summarized by examining the relationship between adverse events and infrastructure development in an active war theater; emphasis was on predicting the occurrence of events and assessing the potential impact of regional infrastructure development efforts on reducing number of such events.
Ph.D.
Doctorate
Industrial Engineering and Management Systems
Engineering and Computer Science
Industrial Engineering

Thesis (Ph. D.)--Florida State University, 2003.
Advisor: Ladislav Kohout, Florida State University, College of Arts and Sciences, Dept. of Computer Science. Title and description from dissertation home page (viewed Mar. 9, 2004). Includes bibliographical references.

Governmental and private institutions rely heavily on reliable computer networks for their everyday business transactions. The downtime of their infrastructure networks may result in millions of dollars in cost. Fault management systems are used to keep today’s complex networks running without significant downtime cost, either by using active techniques or passive techniques. Active techniques impose excessive management traffic, whereas passive techniques often ignore uncertainty inherent in network alarms,leading to unreliable fault identification performance. In this research work, new algorithms are proposed for both types of techniques so as address these handicaps. Active techniques use probing technology so that the managed network can be tested periodically and suspected malfunctioning nodes can be effectively identified and isolated. However, the diagnosing probes introduce extra management traffic and storage space. To address this issue, two new CSP (Constraint Satisfaction Problem)-based algorithms are proposed to minimize management traffic, while effectively maintain the same diagnostic power of the available probes. The first algorithm is based on the standard CSP formulation which aims at reducing the available dependency matrix significantly as means to reducing the number of probes. The obtained probe set is used for fault detection and fault identification. The second algorithm is a fuzzy CSP-based algorithm. This proposed algorithm is adaptive algorithm in the sense that an initial reduced fault detection probe set is utilized to determine the minimum set of probes used for fault identification. Based on the extensive experiments conducted in this research both algorithms have demonstrated advantages over existing methods in terms of the overall management traffic needed to successfully monitor the targeted network system. Passive techniques employ alarms emitted by network entities. However, the fault evidence provided by these alarms can be ambiguous, inconsistent, incomplete, and random. To address these limitations, alarms are correlated using a distributed Dempster-Shafer Evidence Theory (DSET) framework, in which the managed network is divided into a cluster of disjoint management domains. Each domain is assigned an Intelligent Agent for collecting and analyzing the alarms generated within that domain. These agents are coordinated by a single higher level entity, i.e., an agent manager that combines the partial views of these agents into a global one. Each agent employs DSET-based algorithm that utilizes the probabilistic knowledge encoded in the available fault propagation model to construct a local composite alarm. The Dempster‘s rule of combination is then used by the agent manager to correlate these local composite alarms. Furthermore, an adaptive fuzzy DSET-based algorithm is proposed to utilize the fuzzy information provided by the observed cluster of alarms so as to accurately identify the malfunctioning network entities. In this way, inconsistency among the alarms is removed by weighing each received alarm against the others, while randomness and ambiguity of the fault evidence are addressed within soft computing framework. The effectiveness of this framework has been investigated based on extensive experiments. The proposed fault management system is able to detect malfunctioning behavior in the managed network with considerably less management traffic. Moreover, it effectively manages the uncertainty property intrinsically contained in network alarms,thereby reducing its negative impact and significantly improving the overall performance of the fault management system.

Artificial intelligence is the outlet of computer science apprehensive with creating computers that perform as humans. It compromises expert systems, playing games, natural language, and robotics. However, soft computing (SC) varies from the hard (conventional) computing in its tolerant of partial truth, uncertainty, imprecision, and approximation, thus, it models the human mind. The most common SC techniques include neural networks, fuzzy systems, machine learning, and the meta-heuristic stochastic algorithms (e.g., Cellular automata, ant colony optimization, Memetic algorithms, particle swarms, Tabu search, evolutionary computation and simulated annealing. Due to the required accurate diseases analysis, magnetic resonance imaging, computed tomography images and images of other modalities segmentation remains a challenging problem. Over the past years, soft computing approaches attract attention of several researchers for problems solving in medical data applications. Image segmentation is the process that partitioned an image into some groups based on similarity measures. This process is employed for abnormalities volumetric analysis in medical images to identify the disease nature. Recently, meta-heuristic algorithms are conducted to support the segmentation techniques. In the current chapter, different segmentation procedures are addressed. Several meta-heuristic approaches are reported with highlights on their procedures. Finally, several medical applications using meta-heuristic based-approaches for segmentation are discussed.

Fault diagnosis is a complex and fuzzy cognitive process, and soft computing methods and technologies based on Neural Networks (NN) and Fuzzy Logic (FL), have shown great potential in the development of Decision Support Systems (DSS). Dealing with expert (human) knowledge consideration, Computer Aided Diagnosis (CAD) dilemma is one of the most interesting, but also one of the most difficult problems. Among difficulties contributing to challenging nature of this problem, one can mention the need of fine pattern recognition (classification) and decision-making. This Chapter deals with classification and decision-making based on Artificial Intelligence using multiple model approaches under soft computing implying modular Neural Networks (NN) and Fuzzy Logic (FL) for biomedical and industrial applications. The aim of this Chapter is absolutely not to replace specialized human but to suggest decision support tools: hybrid intelligent diagnosis systems with a satisfactory reliability degree for CAD. In this Chapter, a methodology is given in order to design hybrid intelligent diagnosis systems for a large field of biomedical and industrial applications. For this purpose, first, a survey on diagnosis tasks in such applications is presented. Second, fault diagnosis systems are presented. Third, the main steps of hybrid intelligent diagnosis systems are developed, for each step emphasizing problems and suggesting solutions able to ensure the design of hybrid intelligent diagnosis systems with a satisfactory reliability degree. In fact, the main steps discussed are knowledge representation, classification, classifier issued information fusion, and decision-making. Then, the suggested approach is developed for a CAD in biomedicine, from Auditory Brainstem Response (ABR) test, and the prototype design and experimental results are presented. Finally, a discussion is given with regard to the reliability and large application field of the suggested approach.

Fault diagnosis is a complex and fuzzy cognitive process, and soft computing methods and technologies based on Neural Networks (NN) and Fuzzy Logic (FL), have shown great potential in the development of Decision Support Systems (DSS). Dealing with expert (human) knowledge consideration, Computer Aided Diagnosis (CAD) dilemma is one of the most interesting, but also one of the most difficult problems. Among difficulties contributing to challenging nature of this problem, one can mention the need of fine pattern recognition (classification) and decision-making. This Chapter deals with classification and decision-making based on Artificial Intelligence using multiple model approaches under soft computing implying modular Neural Networks (NN) and Fuzzy Logic (FL) for biomedical and industrial applications. The aim of this Chapter is absolutely not to replace specialized human but to suggest decision support tools: hybrid intelligent diagnosis systems with a satisfactory reliability degree for CAD. In this Chapter, a methodology is given in order to design hybrid intelligent diagnosis systems for a large field of biomedical and industrial applications. For this purpose, first, a survey on diagnosis tasks in such applications is presented. Second, fault diagnosis systems are presented. Third, the main steps of hybrid intelligent diagnosis systems are developed, for each step emphasizing problems and suggesting solutions able to ensure the design of hybrid intelligent diagnosis systems with a satisfactory reliability degree. In fact, the main steps discussed are knowledge representation, classification, classifier issued information fusion, and decision-making. Then, the suggested approach is developed for a CAD in biomedicine, from Auditory Brainstem Response (ABR) test, and the prototype design and experimental results are presented. Finally, a discussion is given with regard to the reliability and large application field of the suggested approach.