Parallel implementations of backpropagation neural networks on transputers: a study of training set parallelism
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Singapore
World Scientific
c1996
|
| Schriftenreihe: | Progress in neural processing
3 |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Beschreibung: | Includes bibliographical references (p. 189-199) and index 1. Introduction. 1.1. Multilayer feedforward neural networks. 1.2. The basic BP algorithm. 1.3. Parallelism in the BP algorithm. 1.4. Some parallel implementations -- 2. Transputer topologies for parallel implementation. 2.1. The transputer. 2.2. Topologies. 2.3. Topology chosen in this study. 2.4 .Software used. 2.5. Performance metrics and benchmark problems -- 3. Development of a theoretical model for training set parallelism in a homogeneous array of transputers. 3.1. Time components of parallel transputer implementation. 3.2. Timing aspects of parallelizing the backpropagation algorithm. 3.3. Time components for the parallelized backpropagation algorithm. 3.4. Validation of the Tepoch model -- - 4. Equal distribution of patterns amongst a homogeneous array of transputers. 4.1. Analytical model for time per epoch. 4.2. Validation of the model for equal distribution. 4.3. Optimal number of transputers needed for the case of equal distribution. 4.4. Cost benefits analysis of adding additional processors -- 5. Optimization model for unequal distribution of patterns in a homogeneous array of transputers. 5.1. Constraints for optimization. 5.2. Optimal pattern distribution. 5.3. Validation of the pattern optimization model. 5.4. Experimental results for benchmark problems. 5.5. Locating surplus processors and to find out the optimal number of processors needed to obtain minimum time per epoch -- 6. Optimization model for unequal distribution of patterns in a heterogeneous array of transputers. 6.1. Experimental results for benchmark problems. 6.2. Statistical verification of the optimal epoch time. 6.3. Discussion -- - 7. Pattern allocation schemes using genetic algorithm. 7.1. Optimization algorithm and computational complexity. 7.2. Solution time for optimal pattern. 7.3. Sub-optimal method: Heuristic distribution. 7.4. Genetic algorithm for pattern allocation. 7.5. Comparison between genetic algorithm and MIP. 7.6. Inclusion of 'A Priori' information. 7.7. G.A. with the proposed stopping criterion versus MIP -- A. Comparison between pipelined ring topology and ring topology. A1. Theoretical optimal epoch time for pipelined ring topology. A.2. Theoretical optimal epoch time for ring topology. A.3. Comparison between pipelined ring topology and ring topology -- B. A sample parallel C program -- C. The branch and bound method for solving mixed integer programming problems This book presents a systematic approach to parallel implementation of feedforward neural networks on an array of transputers. The emphasis is on backpropagation learning and training set parallelism. Using systematic analysis, a theoretical model has been developed for the parallel implementation. The model is used to find the optimal mapping to minimize the training time for large backpropagation neural networks. The model has been validated experimentally on several well known benchmark problems. Use of genetic algorithms for optimizing the performance of the parallel implementations is described. Guidelines for efficient parallel implementations are highlighted |
| Beschreibung: | 1 Online-Ressource (xviii, 202 p.) |
| ISBN: | 9789812814968 9812814965 9810226543 9789810226541 |
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| 245 | 1 | 0 | |a Parallel implementations of backpropagation neural networks on transputers |b a study of training set parallelism |c editors, P. Saratchandran, N. Sundararajan, Shou King Foo |
| 264 | 1 | |a Singapore |b World Scientific |c c1996 | |
| 300 | |a 1 Online-Ressource (xviii, 202 p.) | ||
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| 490 | 0 | |a Progress in neural processing |v 3 | |
| 500 | |a Includes bibliographical references (p. 189-199) and index | ||
| 500 | |a 1. Introduction. 1.1. Multilayer feedforward neural networks. 1.2. The basic BP algorithm. 1.3. Parallelism in the BP algorithm. 1.4. Some parallel implementations -- 2. Transputer topologies for parallel implementation. 2.1. The transputer. 2.2. Topologies. 2.3. Topology chosen in this study. 2.4 .Software used. 2.5. Performance metrics and benchmark problems -- 3. Development of a theoretical model for training set parallelism in a homogeneous array of transputers. 3.1. Time components of parallel transputer implementation. 3.2. Timing aspects of parallelizing the backpropagation algorithm. 3.3. Time components for the parallelized backpropagation algorithm. 3.4. Validation of the Tepoch model -- | ||
| 500 | |a - 4. Equal distribution of patterns amongst a homogeneous array of transputers. 4.1. Analytical model for time per epoch. 4.2. Validation of the model for equal distribution. 4.3. Optimal number of transputers needed for the case of equal distribution. 4.4. Cost benefits analysis of adding additional processors -- 5. Optimization model for unequal distribution of patterns in a homogeneous array of transputers. 5.1. Constraints for optimization. 5.2. Optimal pattern distribution. 5.3. Validation of the pattern optimization model. 5.4. Experimental results for benchmark problems. 5.5. Locating surplus processors and to find out the optimal number of processors needed to obtain minimum time per epoch -- 6. Optimization model for unequal distribution of patterns in a heterogeneous array of transputers. 6.1. Experimental results for benchmark problems. 6.2. Statistical verification of the optimal epoch time. 6.3. Discussion -- | ||
| 500 | |a - 7. Pattern allocation schemes using genetic algorithm. 7.1. Optimization algorithm and computational complexity. 7.2. Solution time for optimal pattern. 7.3. Sub-optimal method: Heuristic distribution. 7.4. Genetic algorithm for pattern allocation. 7.5. Comparison between genetic algorithm and MIP. 7.6. Inclusion of 'A Priori' information. 7.7. G.A. with the proposed stopping criterion versus MIP -- A. Comparison between pipelined ring topology and ring topology. A1. Theoretical optimal epoch time for pipelined ring topology. A.2. Theoretical optimal epoch time for ring topology. A.3. Comparison between pipelined ring topology and ring topology -- B. A sample parallel C program -- C. The branch and bound method for solving mixed integer programming problems | ||
| 500 | |a This book presents a systematic approach to parallel implementation of feedforward neural networks on an array of transputers. The emphasis is on backpropagation learning and training set parallelism. Using systematic analysis, a theoretical model has been developed for the parallel implementation. The model is used to find the optimal mapping to minimize the training time for large backpropagation neural networks. The model has been validated experimentally on several well known benchmark problems. Use of genetic algorithms for optimizing the performance of the parallel implementations is described. Guidelines for efficient parallel implementations are highlighted | ||
| 650 | 7 | |a COMPUTERS / Enterprise Applications / Business Intelligence Tools |2 bisacsh | |
| 650 | 7 | |a COMPUTERS / Intelligence (AI) & Semantics |2 bisacsh | |
| 650 | 7 | |a Back propagation (Artificial intelligence) |2 fast | |
| 650 | 7 | |a Neural networks (Computer science) |2 fast | |
| 650 | 7 | |a Parallel processing (Electronic computers) |2 fast | |
| 650 | 7 | |a Transputers |2 fast | |
| 650 | 4 | |a Parallel processing (Electronic computers) | |
| 650 | 4 | |a Neural networks (Computer science) | |
| 650 | 4 | |a Back propagation (Artificial intelligence) | |
| 650 | 4 | |a Transputers | |
| 700 | 1 | |a Sundararajan, N. |e Sonstige |4 oth | |
| 700 | 1 | |a Foo, Shou King |e Sonstige |4 oth | |
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Datensatz im Suchindex
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|---|---|
| any_adam_object | |
| author | Saratchandran, P. |
| author_facet | Saratchandran, P. |
| author_role | aut |
| author_sort | Saratchandran, P. |
| author_variant | p s ps |
| building | Verbundindex |
| bvnumber | BV042969093 |
| collection | ZDB-4-EBU |
| ctrlnum | (OCoLC)828423959 (DE-599)BVBBV042969093 |
| dewey-full | 006.3 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.3 |
| dewey-search | 006.3 |
| dewey-sort | 16.3 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| format | Electronic eBook |
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| id | DE-604.BV042969093 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T07:14:03Z |
| institution | BVB |
| isbn | 9789812814968 9812814965 9810226543 9789810226541 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-028394959 |
| oclc_num | 828423959 |
| open_access_boolean | |
| physical | 1 Online-Ressource (xviii, 202 p.) |
| psigel | ZDB-4-EBU FLA_PDA_EBU |
| publishDate | 1996 |
| publishDateSearch | 1996 |
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| publisher | World Scientific |
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| series2 | Progress in neural processing |
| spelling | Saratchandran, P. Verfasser aut Parallel implementations of backpropagation neural networks on transputers a study of training set parallelism editors, P. Saratchandran, N. Sundararajan, Shou King Foo Singapore World Scientific c1996 1 Online-Ressource (xviii, 202 p.) txt rdacontent c rdamedia cr rdacarrier Progress in neural processing 3 Includes bibliographical references (p. 189-199) and index 1. Introduction. 1.1. Multilayer feedforward neural networks. 1.2. The basic BP algorithm. 1.3. Parallelism in the BP algorithm. 1.4. Some parallel implementations -- 2. Transputer topologies for parallel implementation. 2.1. The transputer. 2.2. Topologies. 2.3. Topology chosen in this study. 2.4 .Software used. 2.5. Performance metrics and benchmark problems -- 3. Development of a theoretical model for training set parallelism in a homogeneous array of transputers. 3.1. Time components of parallel transputer implementation. 3.2. Timing aspects of parallelizing the backpropagation algorithm. 3.3. Time components for the parallelized backpropagation algorithm. 3.4. Validation of the Tepoch model -- - 4. Equal distribution of patterns amongst a homogeneous array of transputers. 4.1. Analytical model for time per epoch. 4.2. Validation of the model for equal distribution. 4.3. Optimal number of transputers needed for the case of equal distribution. 4.4. Cost benefits analysis of adding additional processors -- 5. Optimization model for unequal distribution of patterns in a homogeneous array of transputers. 5.1. Constraints for optimization. 5.2. Optimal pattern distribution. 5.3. Validation of the pattern optimization model. 5.4. Experimental results for benchmark problems. 5.5. Locating surplus processors and to find out the optimal number of processors needed to obtain minimum time per epoch -- 6. Optimization model for unequal distribution of patterns in a heterogeneous array of transputers. 6.1. Experimental results for benchmark problems. 6.2. Statistical verification of the optimal epoch time. 6.3. Discussion -- - 7. Pattern allocation schemes using genetic algorithm. 7.1. Optimization algorithm and computational complexity. 7.2. Solution time for optimal pattern. 7.3. Sub-optimal method: Heuristic distribution. 7.4. Genetic algorithm for pattern allocation. 7.5. Comparison between genetic algorithm and MIP. 7.6. Inclusion of 'A Priori' information. 7.7. G.A. with the proposed stopping criterion versus MIP -- A. Comparison between pipelined ring topology and ring topology. A1. Theoretical optimal epoch time for pipelined ring topology. A.2. Theoretical optimal epoch time for ring topology. A.3. Comparison between pipelined ring topology and ring topology -- B. A sample parallel C program -- C. The branch and bound method for solving mixed integer programming problems This book presents a systematic approach to parallel implementation of feedforward neural networks on an array of transputers. The emphasis is on backpropagation learning and training set parallelism. Using systematic analysis, a theoretical model has been developed for the parallel implementation. The model is used to find the optimal mapping to minimize the training time for large backpropagation neural networks. The model has been validated experimentally on several well known benchmark problems. Use of genetic algorithms for optimizing the performance of the parallel implementations is described. Guidelines for efficient parallel implementations are highlighted COMPUTERS / Enterprise Applications / Business Intelligence Tools bisacsh COMPUTERS / Intelligence (AI) & Semantics bisacsh Back propagation (Artificial intelligence) fast Neural networks (Computer science) fast Parallel processing (Electronic computers) fast Transputers fast Parallel processing (Electronic computers) Neural networks (Computer science) Back propagation (Artificial intelligence) Transputers Sundararajan, N. Sonstige oth Foo, Shou King Sonstige oth http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=532589 Aggregator Volltext |
| spellingShingle | Saratchandran, P. Parallel implementations of backpropagation neural networks on transputers a study of training set parallelism COMPUTERS / Enterprise Applications / Business Intelligence Tools bisacsh COMPUTERS / Intelligence (AI) & Semantics bisacsh Back propagation (Artificial intelligence) fast Neural networks (Computer science) fast Parallel processing (Electronic computers) fast Transputers fast Parallel processing (Electronic computers) Neural networks (Computer science) Back propagation (Artificial intelligence) Transputers |
| title | Parallel implementations of backpropagation neural networks on transputers a study of training set parallelism |
| title_auth | Parallel implementations of backpropagation neural networks on transputers a study of training set parallelism |
| title_exact_search | Parallel implementations of backpropagation neural networks on transputers a study of training set parallelism |
| title_full | Parallel implementations of backpropagation neural networks on transputers a study of training set parallelism editors, P. Saratchandran, N. Sundararajan, Shou King Foo |
| title_fullStr | Parallel implementations of backpropagation neural networks on transputers a study of training set parallelism editors, P. Saratchandran, N. Sundararajan, Shou King Foo |
| title_full_unstemmed | Parallel implementations of backpropagation neural networks on transputers a study of training set parallelism editors, P. Saratchandran, N. Sundararajan, Shou King Foo |
| title_short | Parallel implementations of backpropagation neural networks on transputers |
| title_sort | parallel implementations of backpropagation neural networks on transputers a study of training set parallelism |
| title_sub | a study of training set parallelism |
| topic | COMPUTERS / Enterprise Applications / Business Intelligence Tools bisacsh COMPUTERS / Intelligence (AI) & Semantics bisacsh Back propagation (Artificial intelligence) fast Neural networks (Computer science) fast Parallel processing (Electronic computers) fast Transputers fast Parallel processing (Electronic computers) Neural networks (Computer science) Back propagation (Artificial intelligence) Transputers |
| topic_facet | COMPUTERS / Enterprise Applications / Business Intelligence Tools COMPUTERS / Intelligence (AI) & Semantics Back propagation (Artificial intelligence) Neural networks (Computer science) Parallel processing (Electronic computers) Transputers |
| url | http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=532589 |
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