Showing papers on "Soft computing published in 2002"

Adaptive dynamic programming

Abstract: Unlike the many soft computing applications where it suffices to achieve a "good approximation most of the time," a control system must be stable all of the time. As such, if one desires to learn a control law in real-time, a fusion of soft computing techniques to learn the appropriate control law with hard computing techniques to maintain the stability constraint and guarantee convergence is required. The objective of the paper is to describe an adaptive dynamic programming algorithm (ADPA) which fuses soft computing techniques to learn the optimal cost (or return) functional for a stabilizable nonlinear system with unknown dynamics and hard computing techniques to verify the stability and convergence of the algorithm. Specifically, the algorithm is initialized with a (stabilizing) cost functional and the system is run with the corresponding control law (defined by the Hamilton-Jacobi-Bellman equation), with the resultant state trajectories used to update the cost functional in a soft computing mode. Hard computing techniques are then used to show that this process is globally convergent with stepwise stability to the optimal cost functional/control law pair for an (unknown) input affine system with an input quadratic performance measure (modulo the appropriate technical conditions). Three specific implementations of the ADPA are developed for 1) the linear case, 2) for the nonlinear case using a locally quadratic approximation to the cost functional, and 3) the nonlinear case using a radial basis function approximation of the cost functional; illustrated by applications to flight control.

Data mining in soft computing framework: a survey

Abstract: The present article provides a survey of the available literature on data mining using soft computing. A categorization has been provided based on the different soft computing tools and their hybridizations used, the data mining function implemented, and the preference criterion selected by the model. The utility of the different soft computing methodologies is highlighted. Generally fuzzy sets are suitable for handling the issues related to understandability of patterns, incomplete/noisy data, mixed media information and human interaction, and can provide approximate solutions faster. Neural networks are nonparametric, robust, and exhibit good learning and generalization capabilities in data-rich environments. Genetic algorithms provide efficient search algorithms to select a model, from mixed media data, based on some preference criterion/objective function. Rough sets are suitable for handling different types of uncertainty in data. Some challenges to data mining and the application of soft computing methodologies are indicated. An extensive bibliography is also included.

Web mining in soft computing framework: relevance, state of the art and future directions

Abstract: The paper summarizes the different characteristics of Web data, the basic components of Web mining and its different types, and the current state of the art. The reason for considering Web mining, a separate field from data mining, is explained. The limitations of some of the existing Web mining methods and tools are enunciated, and the significance of soft computing (comprising fuzzy logic (FL), artificial neural networks (ANNs), genetic algorithms (GAs), and rough sets (RSs) are highlighted. A survey of the existing literature on "soft Web mining" is provided along with the commercially available systems. The prospective areas of Web mining where the application of soft computing needs immediate attention are outlined with justification. Scope for future research in developing "soft Web mining" systems is explained. An extensive bibliography is also provided.

Forecasting the NYSE composite index with technical analysis, pattern recognizer, neural networks, and genetic algorithm: a case study in romantic decision support

Abstract: The 21st century is seeing technological advances that make it possible to build more robust and sophisticated decision support systems than ever before. But the effectiveness of these systems may be limited if we do not consider more eclectic (or romantic) options. This paper exemplifies the potential that lies in the novel application and combination of methods, in this case to evaluating stock market purchasing opportunities using the "technical analysis" school of stock market prediction. Members of the technical analysis school predict market prices and movements based on the dynamics of market price and volume, rather than on economic fundamentals such as earnings and market share. The results of this paper support the effectiveness of the technical analysis approach through use of the "bull flag" price and volume pattern heuristic. The romantic approach to decision support exemplified in this paper is made possible by the recent development of: (1) high-performance desktop computing, (2) the methods and techniques of machine learning and soft computing, including neural networks and genetic algorithms, and (3) approaches recently developed that combine diverse classification and forecasting systems. The contribution of this paper lies in the novel application and combination of the decision-making methods and in the nature and superior quality of the results achieved.

The merging of neural networks, fuzzy logic, and genetic algorithms

Abstract: During the last decade, there has been increased use of neural networks (NNs), fuzzy logic (FL) and genetic algorithms (GAs) in insurance-related applications. However, the focus often has been on a single technology heuristically adapted to a problem. While this approach has been productive, it may have been sub-optimal, in the sense that studies may have been constrained by the limitations of the technology and opportunities may have been missed to take advantage of the synergies between the technologies. For example, while NNs have the positive attributes of adaptation and learning, they have the negative attribute of a “black box” syndrome. By the same token, FL has the advantage of approximate reasoning but the disadvantage that it lacks an effective learning capability. Merging these technologies provides an opportunity to capitalize on their strengths and compensate for their shortcomings. This article presents an overview of the merging of NNs, FL and GAs. The topics addressed include the advantages and disadvantages of each technology, the potential merging options, and the explicit nature of the merging.

Incorporating soft computing techniques into a probabilistic intrusion detection system

Abstract: There are a lot of industrial applications that can be solved competitively by hard computing, while still requiring the tolerance for imprecision and uncertainty that can be exploited by soft computing. This paper presents a novel intrusion detection system (IDS) that models normal behaviors with hidden Markov models (HMM) and attempts to detect intrusions by noting significant deviations from the models. Among several soft computing techniques neural network and fuzzy logic are incorporated into the system to achieve robustness and flexibility. The self-organizing map (SOM) determines the optimal measures of audit data and reduces them into appropriate size for efficient modeling by HMM. Based on several models with different measures, fuzzy logic makes the final decision of whether current behavior is abnormal or not. Experimental results with some real audit data show that the proposed fusion produces a viable intrusion detection system. Fuzzy rules that utilize the models based on the measures of system call, file access, and the combination of them produce more reliable performance.

Genetic algorithm with fuzzy fitness function for feature selection

Abstract: Feature selection is an important preprocessing task for any pattern recognition or data mining application. Though lots of well developed statistical and mathematical techniques of feature selection exist they do not match the imprecise and incomplete nature of most of the real world problems. Recently soft computing techniques i.e. neurocomputing, fuzzy logic, genetic algorithm etc. are gaining growing popularity for their remarkable ability of handling real life data like a human being in an environment of uncertainty, imprecision and implicit knowledge. In this work, a genetic algorithm in conjunction with a fuzzy fitness function, a fuzzy measure for evaluation of the quality of a feature has been proposed for feature subset selection. GA based feature selection algorithms are robust but their computation time is high specially when they are used with a classifier for fitness evaluation. The computationally light fuzzy fitness function lowers the computation time of the traditional GA based algorithm with classifier accuracy as the fitness function by separating the two stages feature selection and classification. Simulation over two data sets shows the efficiency of the proposed technique for achieving near optimal solution in practical problems specially when the data set contains a large number of features.

The hierarchical expert tuning of PID controllers using tools of soft computing

Abstract: We present soft computing-based results pertaining to the hierarchical tuning process of PID controllers located within the control loop of a class of nonlinear systems The results are compared with PID controllers implemented either in a stand alone scheme or as a part of conventional gain scheduling structure This work is motivated by the increasing need in the industry to design highly reliable and efficient controllers for dealing with regulation and tracking capabilities of complex processes characterized by nonlinearities and possibly time varying parameters The soft computing-based controllers proposed are hybrid in nature in that they integrate within a well-defined hierarchical structure the benefits of hard algorithmic controllers with those having supervisory capabilities The controllers proposed also have the distinct features of learning and auto-tuning without the need for tedious and computationally extensive online systems identification schemes

Fusion of soft computing and hard computing in industrial applications: an overview

Abstract: Soft computing (SC) is an emerging collection of methodologies which aims to exploit tolerance for imprecision, uncertainty, and partial truth to achieve robustness, tractability, and low total cost. It differs from conventional hard computing (HC) in the sense that, unlike hard computing, it is strongly based on intuition or subjectivity. Therefore, soft computing provides an attractive opportunity to represent the ambiguity in human thinking with real life uncertainty. Fuzzy logic (FL), neural networks (NN), and genetic algorithms (GA) are the core methodologies of soft computing. However, FL, NN, and GA should not be viewed as competing with each other, but synergistic and complementary instead. Considering the number of available journal and conference papers on various combinations of these three methods, it is easy to conclude that the fusion of individual soft computing methodologies has already been advantageous in numerous applications. On the other hand, hard computing solutions are usually more straightforward to analyze; their behavior and stability are more predictable; and, the computational burden of algorithms is typically either low or moderate. These characteristics. are particularly important in real-time applications. Thus, it is natural to see SC and HC as potentially complementary methodologies. Novel combinations of different methods are needed when developing high-performance, cost-effective, and safe products for the demanding global market. We present an overview of applications in which the fusion of soft computing and hard computing has provided innovative solutions for challenging real-world problems. A carefully selected list of references is considered with evaluative discussions and conclusions.

Computational military tactical planning system

Abstract: A computational system called fuzzy-genetic decision optimization combines two soft computing methods, genetic optimization and fuzzy ordinal preference, and a traditional hard computing method, stochastic system simulation, to tackle the difficult task of generating battle plans for military tactical forces. Planning for a tactical military battle is a complex, high-dimensional task which often bedevils experienced professionals. In fuzzy-genetic decision optimization, the military commander enters his battle outcome preferences into a user interface to generate a fuzzy ordinal preference model that scores his preference for any battle outcome. A genetic algorithm iteratively generates populations of battle plans for evaluation in a stochastic combat simulation. The fuzzy preference model converts the simulation results into a fitness value for each population member, allowing the genetic algorithm to generate the next population. Evolution continues until the system produces a final population of high-performance plans which achieve the commander's intent for the mission. Analysis of experimental results shows that co-evolution of friendly and enemy plans by competing genetic algorithms improves the performance of the planning system. If allowed to evolve long enough, the plans produced by automated algorithms had a significantly higher mean performance than those generated by experienced military experts.

Advances in Soft Computing — AFSS 2002

Abstract: Fuzzy rules are conditional pieces of knowledge which can either express constraints on the set of values which are left possible for a variable, given the values of other variables, or accumulate tuples of feasible values. The first type are implicative rules, while the second are based on conjunctions. Consequences of this view on inference and interpolation between sparse rules are presented.

Utilization of soft computing techniques for constructing reliable decision support systems for dealing stocks

Abstract: The use of soft computing techniques such as NNs, GAs, etc. in the financial market has become one of the most exciting and promising application areas. We propose a decision support system (DSS) for dealing in the TOPIX (Tokyo Stock Exchange Prices Indexes), which utilizes neural networks and genetic algorithms. In the proposed system, the neural network is utilized in order to make a forecast of the TOPIX four weeks in the future. The genetic algorithm is utilized in order to find an effective way of dealing. Several computer simulations have been carried out in order to compare the proposed DSS with the other approaches such as the DSS using traditional technical analysis and a buy-and-hold method. These simulations confirm the effectiveness of the proposed DSS.

Soft computing systems : design, management and applications

Abstract: This volume focuses on research developments on intelligent systems in a hybrid environment and its applications in image processing, Internet modelling and data mining. The contributions presented were accepted for the Second International Conference on Hybrid Intelligent Systems (HIS '02).

User profiles and fuzzy logic for web retrieval issues

Abstract: We present a study of the role of user profiles using fuzzy logic in web retrieval processes. Flexibility for user interaction and for adaptation in profile construction becomes an important issue. We focus our study on user profiles, including creation, modification, storage, clustering and interpretation. We also consider the role of fuzzy logic and other soft computing techniques to improve user profiles. Extended profiles contain additional information related to the user that can be used to personalize and customize the retrieval process as well as the web site. Web mining processes can be carried out by means of fuzzy clustering of these extended profiles and fuzzy rule construction. Fuzzy inference can be used in order to modify queries and extract knowledge from profiles with marketing purposes within a web framework. An architecture of a portal that could support web mining technology is also presented.

Soft computing applications in aircraft sensor management and flight control law reconfiguration

Abstract: A sensor management system based on soft computing techniques has been developed and implemented in the flight control system of a small commercial aircraft. Unlike in the conventional sensor management system, the signals from sensors are assigned weights based on fuzzy membership functions and the consolidated signal is computed as a weighted average. This approach improves the quality of the consolidated signal and reduces transients due to sensor failures. This soft voting is extended to soft flight control law reconfiguration. In addition, a virtual sensor has been introduced as an arbitrator which enables the isolation of the failed sensor in the duplex operation and the detection of a sensor failure in the simplex operation. The effectiveness of the proposed methods is demonstrated by using an extensive simulation model of a small commercial aircraft, developed by airframe and control system manufacturers on the basis of an existing business jet. Furthermore, the system has been successfully evaluated and compared to standard techniques by means of pilot-in-the-loop simulations on the Research Flight Simulator of the National Aerospace Laboratory in The Netherlands. This application, developed within a Brite/EuRam research project, is characterized by the effective combination of novel soft computing techniques with standard, well proven methods of the aircraft industry. The properties of the conventional sensor management system have been retained, with the additional advantage that the quality of the consolidated signal is improved, the failure-induced transients are reduced, and the consolidated signal remains available up to the last valid sensor.

Soft computing agents: new trends for designing autonomous systems

Abstract: 1: "Conscious" Software: A Computational View of Mind.- 2: Intelligent Agents in Granular Worlds.- 3: Controlling Effective Introns for Multi-Agent Learning by Means of Genetic Programming.- 4: TalkMine: A Soft Computing Approach to Adaptive Knowledge Recommendation.- 5: A Soft-Computing Distributed Artificial Intelligence Architecture for Intelligent Buildings.- 6: Towards a Multiagent Design Principle: Analyzing an Organizational-Learning Oriented Classifer System.- 7: A Human-Centered Approach for Intelligent Internet Applications.- 8: A Soft Computing Framework for Adaptive Agents.

Hybrid information systems

Abstract: Neural Networks and Applications.- A Full Explanation Facility for a MLP Network that Classifies Low-Back-Pain Patients and for Predicting its Reliability.- Use of Multi-category Proximal SVM for Data Set Reduction.- Neural Techniques in Logo Recognition.- Motion Detection Using Cellular Neural Network.- Speech Separation Based on Higher Order Statistics Using Recurrent Neural Networks.- Speaker Recognition Using Radial Basis Function Neural Networks.- A Multifaceted Investigation into Speech Reading.- Global Optimisation of Neural Networks Using a Deterministic Hybrid Approach.- AppART: An ART Hybrid Stable Learning Neural Network for Universal Function Approximation.- Monitoring System Security Using Neural Networks and Support Vector Machines.- A Hybrid Detection and Classification System for Human Motion Analysis.- Integrated Technique with Neurocomputing for Temporal Video Segmentation.- Matching Data Mining Algorithm Suitability to Data Characteristics Using a Self-Organizing Map.- Perceptual Grouping of Contours via Gated Diffusion of Boundary Signals.- Fuzzy Logic and Applications.- Fusion of Fuzzy System and Conventional Technique to Evaluate Weather and Terrain Effects on the Vehicle Operations.- Soft Computing for Developing Short Term Load Forecasting Models in Czech Republic.- An Induction Algorithm with Selection Significance Based on a Fuzzy Derivative.- Adaptive Database Learning in Decision Support Systems Using Evolutionary Fuzzy Systems: A Generic Framework.- Histogram-Based Fuzzy Clustering and its Comparison to Fuzzy C-Means Clustering in One-Dimensional Data.- Optimizing Linear Programming Technique Using Fuzzy Logic.- Semantics for Fuzzy Disjunctive Programs with Weak Similarity.- An Integration of Fuzzy and Two-Valued Logics on Natural Language Semantics.- Fuzzy Hyperplanes in the Hypothesis Space.- Evolutionary Computation and Other Heuristics.- A Genetic Algorithm for Optimizing Throughput in Non-broadcast WDM Optical Networks.- Solving Trigonometric Identities with Tree Adjunct Grammar Guided Genetic Programming.- Integrated Evolutionary Algorithms.- Evolving Natural Language Parser with Genetic Programming.- A Linear Genetic Programming Approach for Modeling Electricity Demand Prediction in Victoria.- Flexible Generator Maintenance Scheduling in a Practical System Using Fuzzy Logic and Genetic Algorithm.- Information Space Optimization with Real-Coded Genetic Algorithm for Inductive Learning.- A Comparison of GRASP and an Exact Method for Solving a Production and Delivery Scheduling Problem.- Intelligent Agents and Applications.- MEBRL: Memory Evolution Based Reinforcement Learning Algorithm of MAS.- Agent Representation and Communication in CBR-Tutor.- Agent-based Software Engineering and Agent Mediations.- Virtual AI Classroom: A Proposal.- An Argumentation-Based Multi-Agent System for eTourism Dialogue.- Modeling a Distributed Knowledge Management for Cooperative Agents.- Bayesian Methods / Rough Sets and Applications.- Linear Discriminant Text Classification in High Dimension.- A Bayesian Track-before-Detect Algorithm for IR Point Target Detection.- Application of Bayesian Controllers to Dynamic Systems.- An Algorithm for Automatic Generation of a Case Base from a Database Using Similarity-Based Rough Approximation.- A Family of Algorithms for Implementing the Main Concepts of the Rough Set Theory.- Intelligent Data Mining and Information Analysis.- Value of Information Analysis in Dynamic Influence Diagrams.- An Automated Report Generation Tool for the Data Understanding Phase.- Determining the Validity of Clustering for Data Fusion.- The Performance of Small Support Spatial and Temporal Filters for Dim Point Target Detection in Infrared Image Sequences.- Using Petri Nets for Modeling Branch Control of Pipeline Processors.- Extended Vector Annotated Logic Program and its Applications to Robot Action Control and Automated Safety Verification.- Hybrid Intelligent Systems Applications: Reviews and Frameworks.- Overview of Markov Chain Monte Carlo for Statistical Inference and its Application.- Insurance Applications of Soft Computing Technologies.- Teaming Human and Machine: A Conceptual Framework.- Dynamics and Thinking of Social Systems.- Author Index.

A Linear Genetic Programming Approach for Modelling Electricity Demand Prediction in Victoria

Abstract: Genetic programming (GP), a relatively young and growing branch of evolutionary computation is gradually proving to be a promising method of modelling complex prediction and classification problems. This paper evaluates the suitability of a linear genetic programming (LGP) technique to predict electricity demand in the State of Victoria, Australia, while comparing its performance with two other popular soft computing techniques. The forecast accuracy is compared with the actual energy demand. To evaluate, we considered load demand patterns for ten consecutive months taken every 30 minutes for training the different prediction models. Test results show that while the linear genetic programming method delivered satisfactory results, the neuro fuzzy system performed best for this particular application problem, in terms of accuracy and computation time, as compared to LGP and neural networks.

Analysis of hybrid soft and hard computing techniques for forex monitoring systems

Abstract: In a universe with a single currency, there would be no foreign exchange market, no foreign exchange rates, and no foreign exchange. Over the past twenty-five years, the way the market has performed those tasks has changed enormously. The need for intelligent monitoring systems has become a necessity to keep track of the complex forex market. The vast currency market is a foreign concept to the average individual. However, once it is broken down into simple terms, the average individual can begin to understand the foreign exchange market and use it as a financial instrument for future investing. In this paper, we attempt to compare the performance of hybrid soft computing and hard computing techniques to predict the average monthly forex rates one month ahead. The soft computing models considered are a neural network trained by the scaled conjugate gradient algorithm and a neuro-fuzzy model implementing a Takagi-Sugeno fuzzy inference system. We also considered Multivariate Adaptive Regression Splines (MARS), Classification and Regression Trees (CART) and a hybrid CART-MARS technique. We considered the exchange rates of Australian dollar with respect to US dollar, Singapore dollar, New Zealand dollar, Japanese yen and United Kingdom pounds. The models were trained using 70% of the data and remaining was used for testing and validation purposes. It is observed that the proposed hybrid models could predict the forex rates more accurately than all the techniques when applied individually. Empirical results also reveal that the hybrid hard computing approach also improved some of our previous work using a neuro-fuzzy approach.

Soft Computing and Industry: Recent Applications

Abstract: Part I: Keynote Papers.Part II: Intelligent Control.Part III: Classification, Clustering and Optimization.Part IV: Image and Signal Processing.Part V: Agents, Multimedia and Internet.Part VI: Theoretical Advances and New Paradigms.Part VII: Prediction, Design and Diagnosis.

Analysis of hybrid soft and hard computing techniques for forex monitoring systems

Abstract: The need for intelligent monitoring systems has become a necessity to keep track of the complex forex market. The vast currency market is a foreign concept to the average individual. We attempt to compare the performance of hybrid soft computing and hard computing techniques to predict the average monthly forex rates one month ahead. The soft computing models considered are a neural network trained by the scaled conjugate gradient algorithm and a neurofuzzy model implementing a Takagi-Sugeno fuzzy inference system. We also considered multivariate adaptive regression splines (MARS), classification and regression trees (CART) and a hybrid CART-MARS technique. We considered the exchange rates of Australian dollar with respect to US dollar, Singapore dollar, New Zealand dollar, Japanese yen and United Kingdom pounds. The models were trained using 70% of the data and remaining was used for testing and validation purposes. It is observed that the proposed hybrid models could predict the forex rates more accurately than all the techniques when applied individually. Empirical results also reveal that the hybrid hard computing approach also improved some of our previous work using a neuro-fuzzy approach.