Showing papers on "Soft computing published in 2005"

Hard Real-Time Computing Systems

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Harmony search for generalized orienteering problem: best touring in China

Abstract: In order to overcome the drawbacks of mathematical optimization techniques, soft computing algorithms have been vigorously introduced during the past decade. However, there are still some possibilities of devising new algorithms based on analogies with natural phenomena. A nature-inspired algorithm, mimicking the improvisation process of music players, has been recently developed and named Harmony Search (HS). The algorithm has been successfully applied to various engineering optimization problems. In this paper, the HS was applied to a TSP-like NP-hard Generalized Orienteering Problem (GOP) which is to find the utmost route under the total distance limit while satisfying multiple goals. Example area of the GOP is eastern part of China. The results of HS showed that the algorithm could find good solutions when compared to those of artificial neural network.

Fuzzy Cognitive Maps Learning Using Particle Swarm Optimization

Abstract: This paper introduces a new learning algorithm for Fuzzy Cognitive Maps, which is based on the application of a swarm intelligence algorithm, namely Particle Swarm Optimization. The proposed approach is applied to detect weight matrices that lead the Fuzzy Cognitive Map to desired steady states, thereby refining the initial weight approximation provided by the experts. This is performed through the minimization of a properly defined objective function. This novel method overcomes some deficiencies of other learning algorithms and, thus, improves the efficiency and robustness of Fuzzy Cognitive Maps. The operation of the new method is illustrated on an industrial process control problem, and the obtained simulation results support the claim that it is robust and efficient.

A software implementation of a genetic algorithm based approach to network intrusion detection

Abstract: With the rapid expansion of Internet in recent years, computer systems are facing increased number of security threats. Despite numerous technological innovations for information assurance, it is still very difficult to protect computer systems. Therefore, unwanted intrusions take place when the actual software systems are running. Different soft computing based approaches have been proposed to detect computer network attacks. This paper presents a genetic algorithm (GA) based approach to network intrusion detection, and the software implementation of the approach. The genetic algorithm is employed to derive a set of classification rules from network audit data, and the support-confidence framework is utilized as fitness function to judge the quality of each rule. The generated rules are then used to detect or classify network intrusions in a real-time environment. Unlike most existing GA-based approaches, because of the simple representation of rules and the effective fitness function, the proposed method is easier to implement while providing the flexibility to either generally detect network intrusions or precisely classify the types of attacks. Experimental results show the achievement of acceptable detection rates based on benchmark DARPA data sets on intrusions, while no other complementary techniques or relevant heuristics are applied.

A new hybrid method using evolutionary algorithms to train Fuzzy Cognitive Maps

Abstract: A novel hybrid method based on evolutionary computation techniques is presented in this paper for training Fuzzy Cognitive Maps. Fuzzy Cognitive Maps is a soft computing technique for modeling complex systems, which combines the synergistic theories of neural networks and fuzzy logic. The methodology of developing Fuzzy Cognitive Maps relies on human expert experience and knowledge, but still exhibits weaknesses in utilization of learning methods and algorithmic background. For this purpose, we investigate a coupling of differential evolution algorithm and unsupervised Hebbian learning algorithm, using both the global search capabilities of Evolutionary strategies and the effectiveness of the nonlinear Hebbian learning rule. The use of differential evolution algorithm is related to the concept of evolution of a number of individuals from generation to generation and that of nonlinear Hebbian rule to the concept of adaptation to the environment by learning. The hybrid algorithm is introduced, presented and applied successfully in real-world problems, from chemical industry and medicine. Experimental results suggest that the hybrid strategy is capable to train FCM effectively leading the system to desired states and determining an appropriate weight matrix for each specific problem.

Modeling human behavior in user-adaptive systems: Recent advances using soft computing techniques

Abstract: Adaptive Hypermedia systems are becoming more important in our everyday activities and users are expecting more intelligent services from them. The key element of a generic adaptive hypermedia system is the user model. Traditional machine learning techniques used to create user models are usually too rigid to capture the inherent uncertainty of human behavior. In this context, soft computing techniques can be used to handle and process human uncertainty and to simulate human decision-making. This paper examines how soft computing techniques, including fuzzy logic, neural networks, genetic algorithms, fuzzy clustering and neuro-fuzzy systems, have been used, alone or in combination with other machine learning techniques, for user modeling from 1999 to 2004. For each technique, its main applications, limitations and future directions for user modeling are presented. The paper also presents guidelines that show which soft computing techniques should be used according to the task implemented by the application.

Evolving fuzzy rules for due-date assignment problem in semiconductor manufacturing factory

Abstract: This paper presents a fuzzy modeling method proposed by Wang and Mendel for generation of fuzzy rules using data generated from a simulated model that is built from a real factory located in Hsin-Chu science-based park of Taiwan, R.O.C. The fuzzy modeling method is further evolved by a genetic algorithm for due-date assignment problem in manufacturing. By using simulated data, the effectiveness of the proposed method is shown and compared with two other soft computing techniques: multi-layer perceptron neural networks and case-based reasoning. The comparative results indicate that the proposed method is consistently superior to the other two methods.

Soft computing for crisis management and political decision making: the use of genetically evolved fuzzy cognitive maps

Abstract: This paper examines the use of fuzzy cognitive maps (FCMs) as a technique for modeling political and strategic issues situations and supporting the decision-making process in view of an imminent crisis. Its object domain is soft computing using as its basic elements different methods from the areas of fuzzy logic, cognitive maps, neural networks and genetic algorithms. FCMs, more specifically, use notions borrowed from artificial intelligence and combine characteristics of both fuzzy logic and neural networks, in the form of dynamic models that describe a given political setting. The present work proposes the use of the genetically evolved certainty neuron fuzzy cognitive map (GECNFCM) as an extension of certainty neuron fuzzy cognitive maps (CNFCMs) aiming at overcoming the main weaknesses of the latter, namely the recalculation of the weights corresponding to each concept every time a new strategy is adopted. This novel technique combines CNFCMs with genetic algorithms (GAs), the advantage of which lies with their ability to offer the optimal solution without a problem-solving strategy, once the requirements are defined. Using a multiple scenario analysis we demonstrate the value of such a hybrid technique in the context of a model that reflects the political and strategic complexity of the Cyprus issue, as well as the uncertainties involved in it. The issue has been treated on a purely technical level, with distances carefully kept concerning all sides involved in it.

A weight adaptation method for fuzzy cognitive map learning

Abstract: Fuzzy cognitive maps (FCMs) constitute an attractive modeling approach that encompasses advantageous features. The most pronounces are the flexibility in system design, model and control, the comprehensive operation and the abstractive representation of complex systems. The main deficiencies of FCMs are the critical dependence on the initial expert’s beliefs, the recalculation of the weights corresponding to each concept every time a new strategy is adopted and the potential convergence to undesired equilibrium states. In order to update the initial knowledge of human experts and to combine the human experts’ structural knowledge with the training from data, a learning methodology for FCMs is proposed. This learning method, based on nonlinear Hebbian-type learning algorithm, is used to adapt the cause–effect relationships of the FCM model improving the efficiency and robustness of FCMs. A process control problem is presented and its process is investigated using the proposed weight adaptation technique.

Information retrieval in the Web: beyond current search engines

Abstract: In this paper we briefly explore the challenges to expand information retrieval (IR) on the Web, in particular other types of data, Web mining and issues related to crawling. We also mention the main relations of IR and soft computing and how these techniques address these challenges.

Recognition for soft information based on the theory of soft sets

Abstract: In this paper, an appropriate definition of soft information is put forward. In order to discover soft sets application in recognizing soft information patterns, firstly analyze the basic definition of soft sets, make use of table to describe soft sets and give the conception of soft sets reduction according to the characters of soft sets. Then, the method is designed that recognizing soft information patterns by establishing the information table based on soft sets theory; at the same time the solutions are proposed corresponding to the different recognition vectors. This method with good maneuverability can operate collaterally and by batch so as to release the difficulty and complexity in information analysis by some extents.

A fuzzy BP approach for diagnosis and prognosis of bearing faults in induction motors

Abstract: This paper demonstrates a novel and cost-effective approach for diagnosis and prognosis of bearing faults in small and medium size induction motors. Even though, many researchers dealt with the bearing fault diagnosis of induction motors by using traditional and soft computing approaches, the application of these techniques for predicting the remaining life time of electrical equipment is not seen much in the literature. Moreover, individual artificial intelligence (AI) techniques suffer from their own drawbacks, which can overcome by forming a hybrid approach combining the advantages of each technique. Hence, in this paper an attempt has been made to combine neural networks and fuzzy logic and forming a fuzzy back propagation (fuzzy BP) network for identifying the present condition of the bearing and estimate the remaining useful time of the motor. The results obtained from fuzzy BP network are compared with the neural network, which show that the hybrid approach is well suitable for assessing the present condition of the bearing and the time available for the replacement of the bearing.

Computational Intelligence in Data Mining

Abstract: Janos Abonyi and Balazs FeilUniversity of Veszprem, Department of Process Engineering,P.O. Box 158, H-8201 Veszprem, Hungary, abonyij@fmt.vein.huwww.fmt.vein.hu/softcompAjith AbrahamSchool of Computer Science and Engineering,Chung-Ang University, Seoul, S. Korea, ajith.abraham@ieee.orghttp://ajith.softcomputing.netKeywords: KDD, Computational Intelligence, Soft Computing, Fuzzy Classifier System,Rule Base Reduction, VisualizationReceived: December 20, 2004This paper is aimed to give a comprehensive view about the links between computational intelligence anddata mining. Further, a case study is also given in which the extracted knowledge is represented by fuzzyrule-based expert systems obtained by soft computing based data mining algorithms. It is recognized thatboth model performance and interpretability are of major importance, and effort is required to keep theresulting rule bases small and comprehensible. Therefore, CI technique based data mining algorithms havebeen developed for feature selection, feature extraction, model optimization and model reduction (rule basesimplification). Application of these techniques is illustrated using the Wine data classification problem.The results illustrate that that CI based tools can be applied in a synergistic manner though the nine stepsof knowledge discovery.Povzetek:

Energy saving in a wastewater treatment process: an application of fuzzy logic control.

Abstract: Many uncertain factors affect the operation of Wastewater Treatment Plants. Due to the complexity of biological wastewater treatment processes, classical methods show significant difficulties when trying to control them automatically. Consequently soft computing techniques and, specifically, fuzzy logic appears to be a good candidate for controlling these ill-defined, time-varying and non-linear systems. This paper describes the development and implementation of a Fuzzy Logic Controller to regulate the aeration in the Taradell Wastewater Treatment Plant. The main goal of this control process is to save energy without decreasing the quality of the effluent discharged. The fuzzy controller integrates the information coming from two different signals: the Dissolved Oxygen and Oxidation-Reduction Potential values. The simulation results proved that fuzzy logic is a good tool for controlling the aeration of the wastewater treatment plant. The results obtained show that energy savings of more than 10% can be ac...

A Review of: “Static and Dynamic Neural Networks”

Abstract: Soft computing is an important branch of study in the area of intelligent and knowledge-based systems. It has effectively complemented conventional artificial intelligence in the area of machine in...