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101 0 $aeng
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181   $ctxt
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183   $acr
200 00$aFocus on artificial neural networks$b[electronic resource] /$fJohn A. Flores, editor
210   $aNew York $cNova Science Publishers$dc2011
215   $a1 online resource (426 p.)
225 0 $aMathematics research developments
300   $aDescription based upon print version of record.
311   $a1-61324-285-9 
320   $aIncludes bibliographical references and index.
327   $a""FOCUS ON ARTIFICIAL NEURAL NETWORKS ""; ""FOCUS ON ARTIFICIAL NEURAL NETWORKS ""; ""CONTENTS ""; ""PREFACE ""; ""APPLICATION OF ARTIFICIAL NEURAL NETWORKS (ANNS) IN DEVELOPMENT OF PHARMACEUTICAL MICROEMULSIONS ""; ""1. INTRODUCTION ""; ""2. ARTIFICIAL NEURAL NETWORKS (ANNS) ""; ""3. MICROEMULSIONS ""; ""4. APPLICATION OF ANNS IN THE DEVELOPMENT OF MICROEMULSION DRUG DELIVERY SYSTEMS  ""; ""4.1. Prediction of Phase Behaviour ""; ""4.1.1. The influence of ANNs type/architecture  ""; ""4.2. Screening of the Microemulsion Constituents ""
327   $a""4.3. Prediction of Structural Features of Microemulsions  """"5. CONCLUSION  ""; ""Symbols and Terminologies ""; ""REFERENCES ""; ""INVESTGATIONS OF APPLICATION OF ARTIFICIAL NEURAL NETWORK FOR FLOW SHOP SCHEDULING PROBLEMS ""; ""ABSTRACT ""; ""1.0 INTRODUCTION ""; ""1.1. Flow Shop Scheduling""; ""1.2. Methodologies used In Flow shop Scheduling ""; ""2.0. ANN APPROACH FOR SCHEDULING  A BICRITERION FLOW SHOP ""; ""2.1. Problem Description ""; ""2.2. Architecture of the Proposed System ""; ""2.2.1. Initial learning stage""; ""2.2.2. Implementation stage ""
327   $a""2.3. Bidirectional Neural Network Structure  """"2.4. An Illustration ""; ""2.5. Results and Discussions ""; ""3.0. ANN APPROACH FOR SCHEDULING A MULTI CRITERION FLOW SHOP""; ""3.1. Illustration ""; ""3.2. Results and Discussions ""; ""4.0. A HYBRID NEURAL NETWORK-META HEURISTIC APPROACH FOR PERMUTATION FLOW SHOP SCHEDULING ""; ""4.1. Introduction  ""; ""4.2. Architecture of the ANN  ""; ""4.3. Methodology ""; ""4.4. Results and Discussion ""; ""4.4.1. Sulimana???s heuristic""; ""4.4.2. Genetic algorithm ""; ""Generation of initial population""; ""4.4.3. Simulated annealing ""
327   $a""4.5. Results and Discussions """"4.6. Inferences ""; ""5.0. CONCLUSIONS AND FUTURE DIRECTIONS ""; ""REFERENCES ""; ""ARTIFICIAL NEURAL NETWORKS IN ENVIRONMENTAL SCIENCES AND CHEMICAL ENGINEERING ""; ""ABSTRACT ""; ""INTRODUCTION ""; ""BRIEF DESCRIPTION OF ANN ""; ""LITERATURE REVIEW ""; ""ENVIRONMENTAL SCIENCES ""; ""CHEMICAL ENGINEERING ""; ""Modelling ""; ""Control""; ""Software Sensors ""; ""CONCLUSIONS ""; ""ACKNOWLEDGMENTS ""; ""REFERENCES ""; ""ESTABLISHING PRODUCTIVITY INDICES FOR WHEAT IN THE ARGENTINE PAMPAS BY AN ARTIFICIAL NEURAL NETWORK APPROACH""; ""ABSTRACT ""
327   $a""ENVIRONMENTAL FACTORS CONTROLLING WHEAT YIELD IN THE PAMPAS """"Attempts for Predicting Wheat Yield in the Pampas Using Regression Techniques ""; ""Use of Artificial Neural Networks to Predict Wheat Yield ""; ""Establishing Productivity Indices by an Artificial Neural Network Approach""; ""CONCLUDING REMARKS""; ""REFERENCES ""; ""DESIGN OF ARTIFICIAL NEURAL NETWORK PREDICTORS IN MECHANICAL SYSTEMS PROBLEMS ""; ""ABSTRACT ""; ""1. INTRODUCTION ""; ""2. ARTIFICIAL NEURAL NETWORKS (ANNS) ""; ""2.1. Feedforward Neural Networks ""; ""2.2. Recurrent Neural Networks ""
327   $a""2.1.1. Back Propagation neural network (BPNN) ""
410  0$aMathematics Research Developments
606   $aNeural networks (Computer science)
606   $aArtificial intelligence
615  0$aNeural networks (Computer science)
615  0$aArtificial intelligence.
676   $a006.3/2
701   $aFlores$b John A$01470912
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801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910778821903321
996   $aFocus on artificial neural networks$93682993
997   $aUNINA