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|a Focus on artificial neural networks /
|c John A. Flores, editor.
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|a New York :
|b Nova Science Publishers,
|c ©2011.
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|a 1 online resource (xiv, 410 pages) :
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|a Mathematics research developments
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|a Includes bibliographical references and index.
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|a Print version record.
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|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
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| 505 |
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|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
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| 505 |
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|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. Suliman�s heuristic -- 4.4.2. Genetic algorithm -- Generation of initial population -- 4.4.3. Simulated annealing
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|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
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| 505 |
8 |
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|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
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|a Neural networks (Computer science)
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|x Neural Networks.
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|a Flores, John A.
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|i has work:
|a Focus on artificial neural networks (Text)
|1 https://id.oclc.org/worldcat/entity/E39PCGgV3gdkM3HTRqPD8MphgX
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|d New York : Nova Science Publishers, ©2011
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