Topic
Soft computing
About: Soft computing is a research topic. Over the lifetime, 6710 publications have been published within this topic receiving 118508 citations.
Papers published on a yearly basis
Papers
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01 Jul 1994TL;DR: 1. The Evolution of Expert Systems, L.O. Hall and A. Kandel, and Applications of Fuzzy Expert Systems in Integrated Oil Exploration, F. Aminzadeh.
Abstract: 1. The Evolution of Expert Systems, L.O. Hall and A. Kandel. 2. Applications of Fuzzy Expert Systems in Integrated Oil Exploration, F. Aminzadeh. 3. Hardware Applications of Fuzzy Logic Control, M. Jamshidi, R. Marchbanks, E. Kristjansson, K. Kumbla, R. Kelsey, and D. Barak. 4. Fuzzy Expert Systems, R. A. Aliev. 5. Preprocessing Fuzzy Production Rules, M. Schneider, G. Chew, A. Kandel, and G. Langholz. 6. Computational Neural Architectures for Control Applications, L. Jin, M. M. Gupta, and P.N. Nikiforuk. 7. The Application of Artificial Neural Networks in Editing Noisy Seismic Data, X. Zhang and Y. Li. 8. Foundations of Fuzzy Neural Computations, M. Gupta and H. Ding. 9. A Control Algorithm for Knowledge-Based Tecture Image Segmentation, Z. Zhang, Z. Lang, R. E. Scarberry, and M. Simaan. 10. Knowledge-Based On-Line Scheduling for Flexible Manufacturing, I. Hatono and H. Tamura. 11. Coordination of Distributed Intelligent Systems, M. Kamel and H. Ghenniwa. 12. Collaborative Work Based on Multiagent Architectures: A Methodological Perspective, B. Moulin and L. Cloutier. Author Index. Subject Index.
27 citations
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01 Jan 2006-Compel-the International Journal for Computation and Mathematics in Electrical and Electronic Engineering
TL;DR: In this article, an off-line identification of induction motor (IM) parameters at standstill using evolutionary algorithms (EAs) has been proposed to cope with ill-behaved problem domains exhibiting attributes such as discontinuity, time-variance, randomness, and, what is particularly important in this application, the abilit...
Abstract: Purpose – The paper sets out to deal with the off‐line identification of induction motor (IM) parameters at standstill. Determination of values of the IM parameters is essential in sensorless drives with regard to accuracy and quality of the control system.Design/methodology/approach – The presented identification method is based on the reconstruction of stator current response to the forced stator voltage step change; thus the cost function is defined in the classical form of the mean squared error between the computed and experimental data. The identification via evolutionary algorithms (EAs) is presented. The considered problem is continuous and thus a continuous version of EA is suggested as more suitable.Findings – This approach has been shown to have several advantages over classical optimisation methods like the ability to cope with ill‐behaved problem domains exhibiting attributes such as: discontinuity, time‐variance, randomness, and, what is particularly important in this application, the abilit...
27 citations
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01 Jul 2010
TL;DR: This study shows that CO2RBFN obtains RBFNs with an appropriate balance between accuracy and simplicity, outperforming the other methods considered.
Abstract: This paper presents a new evolutionary cooperative---competitive algorithm for the design of Radial Basis Function Networks (RBFNs) for classification problems. The algorithm, CO2RBFN, promotes a cooperative---competitive environment where each individual represents a radial basis function (RBF) and the entire population is responsible for the final solution. The proposal considers, in order to measure the credit assignment of an individual, three factors: contribution to the output of the complete RBFN, local error and overlapping. In addition, to decide the operators' application probability over an RBF, the algorithm uses a Fuzzy Rule Based System. It must be highlighted that the evolutionary algorithm considers a distance measure which deals, without loss of information, with differences between nominal features which are very usual in classification problems. The precision and complexity of the network obtained by the algorithm are compared with those obtained by different soft computing methods through statistical tests. This study shows that CO2RBFN obtains RBFNs with an appropriate balance between accuracy and simplicity, outperforming the other methods considered.
27 citations
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TL;DR: The proposed PSO‐FCM algorithm is compared with fuzzy C‐means (FCM) and particle swarm optimization (PSO) algorithms using comparison factors such as mean square error (MSE), peak signal to noise ratio (PSNR), entropy (energy function), Jaccard (Tanimoto Coefficient) index, dice overlap index and memory requirement for processing the algorithm.
Abstract: Tissues in brain are the most complicated parts of our body, a clear examination and study are therefore required by a radiologist to identify the pathologies. Normal magnetic resonance MR scanner is capable of producing brain images with bounded tissues, where unique and segregated views of the tissues are required. A distinguished view upon the images is manually impossible and can be subjected to operator errors. With the assistance of a soft computing technique, an automated unique segmentation upon the brain tissues along with the identification of the tumor region can be effectively done. These functionalities assist the radiologist extensively. Several soft computing techniques have been proposed and one such technique which is being proposed is PSO-based FCM algorithm. The results of the proposed algorithm is compared with fuzzy C-means FCM and particle swarm optimization PSO algorithms using comparison factors such as mean square error MSE, peak signal to noise ratio PSNR, entropy energy function, Jaccard Tanimoto Coefficient index, dice overlap index and memory requirement for processing the algorithm. The efficiency of the PSO-FCM algorithm is verified using the comparison factors.
27 citations
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01 Jan 2003
TL;DR: This paper discusses uncertainty in Measurement, Soft Computing, Real-time Measurement and Information Processing in a Modern Brewery, and a Fuzzy Classifier with Pyramidal Membership Functions.
Abstract: Uncertainty in Measurement: Some Thoughts about its Expressing and Processing.- Why Two Sigma? A Theoretical Justification for an Empirical Measurement Practice.- Fuzzy Linguistic Scales: Definition, Properties and Applications.- A Fuzzy Shape Specification System to Support Design for Aesthetics.- Generating Membership Functions for a Noise Annoyance Model from Experimental Data.- An Exegesis of Data Fusion.- Possibilistic Logic: A Theoretical Framework for Multiple Source Information Fusion.- Automated Adaptive Situation Assessment.- Soft Computing, Real-time Measurement and Information Processing in a Modern Brewery.- The Aggregation of Industrial Performance Information by the Choquet Fuzzy Integral.- Computing Image with an Analog Circuit Inspired by the Outer Retinal Network.- Extending the Decision Accuracy of a Bioinformatics System.- On Fuzzy Controllers Having Radial Basis Transfer Functions.- Evolutionary Scene Recognition and Simultaneous Position/Orientation Detection.- Evolutionary Dynamics Identification of Multi-Link Manipulators Using Runge - Kutta - Gill RBF Networks.- Towards Reliable Sub-Division of Geological Areas: Interval Approach.- A Fuzzy Classifier with Pyramidal Membership Functions.- A Comparison of Soft Computing and Traditional Approaches for Risk Classification and Claim Cost Prediction in the Automobile Insurance Industry.- Evolutionary Rule Generation and its Application to Credit Scoring.- Social Fuzziology in Action: Acquisition and Making Sense of Social Information.
27 citations