LEADER 05534nam 2200685 a 450 001 9910788961703321 005 20230124192104.0 010 $a1-283-43330-3 010 $a9786613433305 010 $a981-4280-15-1 035 $a(CKB)3400000000016205 035 $a(EBL)3050908 035 $a(OCoLC)775586436 035 $a(SSID)ssj0000646291 035 $a(PQKBManifestationID)12215936 035 $a(PQKBTitleCode)TC0000646291 035 $a(PQKBWorkID)10685828 035 $a(PQKB)10662690 035 $a(MiAaPQ)EBC3050908 035 $a(WSP)00007375 035 $a(Au-PeEL)EBL3050908 035 $a(CaPaEBR)ebr10524591 035 $a(CaONFJC)MIL343330 035 $a(EXLCZ)993400000000016205 100 $a20110922d2011 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aIntegration of swarm intelligence and artificial neutral network$b[electronic resource] /$fSatchidananda Dehuri, Susmita Ghosh, Sung-bae Cho, editors 210 $aHackensack, N.J. ;$aLondon $cWorld Scientific$d2011 215 $a1 online resource (352 p.) 225 1 $aSeries in machine perception and artificial intelligence ;$vv. 78 300 $aDescription based upon print version of record. 311 $a981-4280-14-3 320 $aIncludes bibliographical references and indexes. 327 $aContents; Preface; Chapter 1 Swarm Intelligence and Neural Networks; 1.1. Introduction; 1.2. Swarm Intelligence; 1.2.1. Particle Swarm Optimization; 1.2.2. Ant Colony Optimization; 1.2.3. Bee Colony Optimization; 1.3. Neural Networks; 1.3.1. Evolvable Neural Network; 1.3.2. Higher Order Neural Network; 1.3.3. Pi (?)-Sigma (?) Neural Networks; 1.3.4. Functional Link Artificial Neural Network; 1.3.5. Ridge Polynomial Neural Networks (RPNNs); 1.4. Summary and Discussion; References; Chapter 2 Neural Network and Swarm Intelligence for Data Mining; 2.1. Introduction; 2.2. Testbeds for Data Mining 327 $a2.2.1. Fisher Iris Data2.2.2. Pima - Diabetes Data; 2.2.3. Shuttle Data; 2.2.4. Classification Efficiency; 2.3. Neural Network for Data Mining; 2.3.1. Multi-Layer Perceptron (MLP); 2.3.2. Radial Basis Function Network; 2.4. Swarm Intelligence for Data Mining; 2.4.1. Ant Miner; 2.4.2. Artificial Bee Colony; 2.4.3. Particle Swarm Optimization; 2.5. Comparative Study; 2.6. Conclusions and Outlook; Acknowledgments; References; Chapter 3 Multi-Objective Ant Colony Optimization: A Taxonomy and Review of Approaches; 3.1. Introduction; 3.2. Ant Colony Optimization 327 $a3.3. Basic Concepts of Multi-Objective Optimization3.4. The ACO Metaheuristic for MOOPs in the Literature; 3.5. ACO Variants for MOOP: A Refined Taxonomy; 3.6. Promising Research Areas; 3.7. Conclusions; Acknowledgments; References; Chapter 4 Recurrent Neural Networks with Discontinuous Activation Functions for Convex Optimization; 4.1. Introduction; 4.2. Related Definitions and Lemmas; 4.3. For Linear Programming; 4.3.1. Model Description and Convergence Results; 4.3.2. Simulation Results; 4.4. For Quadratic Programming; 4.4.1. Model Description; 4.4.2. Convergence Results 327 $a4.4.3. Simulation Results4.5. For Non-Smooth Convex Optimization Subject to Linear Equality Constraints; 4.5.1. Model Description and Convergence Results; 4.5.2. Constrained Least Absolute Deviation; 4.6. Application to k-Winners-Take-All; 4.6.1. LP-Based Model; 4.6.2. QP-Based Model; 4.6.3. Simulation Results; 4.7. Concluding Remarks; Acknowledgments; References; Chapter 5 Automated Power Quality Disturbance Classification Using Evolvable Neural Network; 5.1. Introduction; 5.2. Wavelet Transform (WT); 5.3. Brief Overview of Neural Network Classifiers 327 $a5.4. Overview of Particle Swarm Optimization5.5. Signal Generation, Feature Extraction and Classification; 5.6. Results and Discussion; 5.7. Conclusions; References; Chapter 6 Condition Monitoring and Fault Diagnosis Using Intelligent Techniques; 6.1. Introduction; 6.2. Methodology; 6.2.1. Hardware Specification, System Setup and Audio Data Generation; 6.2.2. Data Pre-Processing; 6.2.3. Data Classification Techniques; 6.2.4. Signal Segregation using Independent Component Analysis; 6.3. Experimental Details; 6.3.1. Pre-Processing 327 $a6.3.2. Method 1: Artificial Neural Network Setup for Engine Classification 330 $aThis book provides a new forum for the dissemination of knowledge in both theoretical and applied research on swarm intelligence (SI) and artificial neural network (ANN). It accelerates interaction between the two bodies of knowledge and fosters a unified development in the next generation of computational model for machine learning. To the best of our knowledge, the integration of SI and ANN is the first attempt to integrate various aspects of both the independent research area into a single volume. 410 0$aSeries in machine perception and artificial intelligence ;$vv. 78. 606 $aSwarm intelligence 606 $aNeural networks (Computer science) 615 0$aSwarm intelligence. 615 0$aNeural networks (Computer science) 676 $a006.3 701 $aDehuri$b Satchidananda$01487338 701 $aGhosh$b Susmita$01574164 701 $aCho$b Sung-Bae$01487339 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910788961703321 996 $aIntegration of swarm intelligence and artificial neutral network$93850267 997 $aUNINA