Soft computing

About: Soft computing is a research topic. Over the lifetime, 6710 publications have been published within this topic receiving 118508 citations.

Particle Swarm Optimization based support vector machine for damage level prediction of non-reshaped berm breakwater

Abstract: Particle Swarm Optimization (PSO) is used to optimize the support vector machine (SVM).Models are trained on the data set obtained from experimental data.PSO-SVM with polynomial kernel function performs better than other kernel functions.Different soft computing models results are compared.PSO-SVM is computationally efficient as compared to ANFIS. The damage analysis of coastal structure is very much essential for better and safe design of the structure. In the past, several researchers have carried out physical model studies on non-reshaped berm breakwaters, but failed to give a simple mathematical model to predict damage level for non-reshaped berm breakwaters by considering all the boundary conditions. This is due to the complexity and non-linearity associated with design parameters and damage level determination of non-reshaped berm breakwater. Soft computing tools like Artificial Neural Network, Fuzzy Logic, Support Vector Machine (SVM), etc, are successfully used to solve complex problems. In the present study, SVM and hybrid of Particle Swarm Optimization (PSO) with SVM (PSO-SVM) are developed to predict damage level of non-reshaped berm breakwaters. Optimal kernel parameters of PSO-SVM are determined by PSO algorithm. Both the models are trained on the data set obtained from experiments carried out in Marine Structures Laboratory, Department of Applied Mechanics and Hydraulics, National Institute of Technology Karnataka, Surathkal, India. Results of both models are compared in terms of statistical measures, such as correlation coefficient, root mean square error and scatter index. The PSO-SVM model with polynomial kernel function outperformed other SVM models.

Boosting Ensemble of Relational Neuro-fuzzy Systems

Abstract: In the paper a boosting ensemble of neuro-fuzzy relational systems is created. Rules in relational fuzzy systems are more flexible than rules in linguistic fuzzy systems because of the additional weights in rule consequents. The weights come from an additional binary relation. Thanks to this, input and output fuzzy sets are related to each other with a certain degree. The size of the relations is determined by the number of input fuzzy sets and the number of output fuzzy sets. Simulations performed on popular benchmarks show that the proposed ensemble outperforms other learning systems.

Computationally intelligent hybrid systems : the fusion of soft computing and hard computing

Abstract: Contributors.Foreword (David B. Fogel).Preface.Editor's Introduction to Chapter 1.1 INTRODUCTION TO FUSION OF SOFT COMPUTING AND HARD COMPUTING (Seppo J. Ovaska).1.1 Introduction.1.2 Structural Categories.1.3 Characteristic Features.1.4 Characterization of Hybrid Applications.1.5 Conclusions and Discussion.References.Editor's Introduction to Chapter 2.2 GENERAL MODEL FOR LARGE-SCALE PLANT APPLICATION (Akimoto Kamiya).2.1 Introduction.2.2 Control System Architecture.2.3 Forecasting of Market Demand.2.4 Scheduling of Processes.2.5 Supervisory Control.2.6 Local Control.2.7 General Fusion Model and Fusion Categories.2.8 Conclusions.References .Editor's Introduction to Chapter 3.3 ADAPTIVE FLIGHT CONTROL: SOFT COMPUTING WITH HARD CONSTRAINTS (Richard E. Saeks).3.1 Introduction.3.2 The Adaptive Control Algorithms.3.3 Flight Control.3.4 X-43A-LS Autolander.3.5 LOFLYTE Optimal Control.3.6 LOFLYTE Stability Augmentation.3.7 Design for Uncertainty with Hard Constraints.3.8 Fusion of Soft Computing and Hard Computing.3.9 Conclusions.References.Editor's Introduction to Chapter 4.4 SENSORLESS CONTROL OF SWITCHED RELUCTANCE MOTORS (Adrian David Cheok).4.1 Introduction.4.2 Fuzzy Logic Model.4.3 Accuracy Enhancement Algorithms.4.4 Simulation Algorithm and Results.4.5 Hardware and Software Implementation.4.6 Experimental Results.4.7 Fusion of Soft Computing and Hard Computing.4.8 Conclusion and Discussion.References.Editor's Introduction to Chapter 5.5 ESTIMATION OF UNCERTAINTY BOUNDS FOR LINEAR AND NONLINEAR ROBUST CONTROL (Gregory D. Buckner).5.1 Introduction.5.2 Robust Control of Active Magnetic Bearings.5.3 Nominal H1 Control of the AMB Test Rig.5.4 Estimating Modeling Uncertainty for H1 Control of the AMB Test Rig.5.5 Nonlinear Robust Control of the AMB Test Rig.5.6 Estimating Model Uncertainty for SMC of the AMB Test Rig.5.7 Fusion of Soft Computing and Hard Computing.5.8 Conclusion.References.Editor's Introduction to Chapter 6.6 INDIRECT ON-LINE TOOL WEAR MONITORING (Bernhard Sick).6.1 Introduction.6.2 Problem Description and Monitoring Architecture.6.3 State of the Art.6.4 New Solution.6.5 Experimental Results.6.6 Fusion of Soft Computing and Hard Computing.6.7 Summary and Conclusions.References.Editor's Introduction to Chapter 7.7 PREDICTIVE FILTERING METHODS FOR POWER SYSTEMS APPLICATIONS (Seppo J. Ovaska).7.1 Introduction.7.2 Multiplicative General-Parameter Filtering.7.3 Genetic Algorithm for Optimizing Filter Tap Cross-Connections.7.4 Design of Multiplierless Basis Filters by Evolutionary Programming.7.5 Predictive Filters for Zero-Crossings Detector.7.6 Predictive Filters for Current Reference Generators.7.7 Fusion of Soft Computing and Hard Computing.7.8 Conclusion.References.Appendix 7.1: Coefficients of 50-Hz Sinusoid-Predictive FIR Filters.Editor's Introduction to Chapter 8.8 INTRUSION DETECTION FOR COMPUTER SECURITY (Sung-Bae Cho and Sang-Jun Han).8.1 Introduction.8.2 Related Works.8.3 Intrusion Detection with Hybrid Techniques.8.4 Experimental Results.8.5 Fusion of Soft Computing and Hard Computing.8.6 Concluding Remarks.References.Editor's Introduction to Chapter 9.9 EMOTION GENERATING METHOD ON HUMAN-COMPUTER INTERFACES (Kazuya Mera and Takumi Ichimura).9.1 Introduction.9.2 Emotion Generating Calculations Method.9.3 Emotion-Oriented Interaction Systems.9.4 Applications of Emotion-Oriented Interaction Systems.9.5 Fusion of Soft Computing and Hard Computing.9.6 Conclusion.References.Editor's Introduction to Chapter 10.10 INTRODUCTION TO SCIENTIFIC DATA MINING: DIRECT KERNEL METHODS AND APPLICATIONS (Mark J. Embrechts, Boleslaw Szymanski, and Karsten Sternickel).10.1 Introduction.10.2 What Is Data Mining?10.3 Basic Definitions for Data Mining.10.4 Introduction to Direct Kernel Methods.10.5 Direct Kernel Ridge Regression.10.6 Case Study #1: Predicting the Binding Energy for Amino Acids.10.7 Case Study #2: Predicting the Region of Origin for Italian Olive Oils.10.8 Case Study #3: Predicting Ischemia from Magnetocardiography.10.9 Fusion of Soft Computing and Hard Computing.10.10 Conclusions.References.Editor's Introduction to Chapter 11.11 WORLD WIDE WEB USAGE MINING (Ajith Abraham).11.1 Introduction.11.2 Daily and Hourly Web Usage Clustering.11.3 Daily and Hourly Web Usage Analysis.11.4 Fusion of Soft Computing and Hard Computing.11.5 Conclusions.References.INDEX.ABOUT THE EDITOR.

A new approach to solve multi-objective multi-choice multi-item Atanassov's intuitionistic fuzzy transportation problem using chance operator

Abstract: In some practical situations the decision maker is interested in setting multi aspiration levels for objectives that may not be expressed in a specific manner. So in this paper, Atanassov's intuitionistic fuzzy transportation problem with multi-item, multi-objective function assuming multiple choices is considered. We have modeled multi-objective multi-choice multi-item Atanassov's intuitionistic fuzzy transportation problem (MMMIFTP), and its several special cases. Possibility, necessity and credibility measures for Atanassov's intuitionistic fuzzy numbers for the first time have been developed here. Solution methodology of those models using chance operator has been discussed. A real life example is presented to illustrate proposed models numerically and the results are compared. The optimal results are obtained by using three different soft computing techniques (i) Interactive satisfied method, (ii) Global criteria method and (iii) Goal programming method.