9786204727783 - Artificial Neural Network: Incorporating Optimized Algorithm de K, Thippeswamy (5 resultados)

Taschenbuch. Condición: Neu. Artificial Neural Network | Incorporating Optimized Algorithm | Thippeswamy K | Taschenbuch | Englisch | 2021 | LAP LAMBERT Academic Publishing | EAN 9786204727783 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Artificial neural networks are complex networks emulating the manner human rational neurons process data. They have been widely used in prediction, clustering, classification, and association. The training algorithms that determine the network weights are almost the most important factor that influences the neural network's performance. Lately several meta-heuristic and Evolutionary algorithms are employed to optimize neural networks weights to realize higher neural performance.To solve complex computational problems many meta-heuristic optimization algorithms have been developed. A meta-heuristic is a higher-level procedure designed to discover, create, or select a heuristic that may provide a sufficiently good solution to an optimization problem, especially with incomplete or imperfect information or limited computation capacity. Meta-heuristics may make limited assumptions about the optimization problem being solved, and so they may be usable for a variety of problems. Many meta-heuristics implement some form of stochastic optimization so that the solution found is dependent on the set of random variables generated. 76 pp. Englisch.

Kartoniert / Broschiert. Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: K ThippeswamyDr K. Thippeswamy, Professor, Computer Science & Engineering, Visvesvaraya Technological University, Mysuru, Karnataka, India, is the author of many titles including `Unstructured Data Classification: Uncertain Nearest N.

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Artificial neural networks are complex networks emulating the manner human rational neurons process data. They have been widely used in prediction, clustering, classification, and association. The training algorithms that determine the network weights are almost the most important factor that influences the neural network's performance. Lately several meta-heuristic and Evolutionary algorithms are employed to optimize neural networks weights to realize higher neural performance.To solve complex computational problems many meta-heuristic optimization algorithms have been developed. A meta-heuristic is a higher-level procedure designed to discover, create, or select a heuristic that may provide a sufficiently good solution to an optimization problem, especially with incomplete or imperfect information or limited computation capacity. Meta-heuristics may make limited assumptions about the optimization problem being solved, and so they may be usable for a variety of problems. Many meta-heuristics implement some form of stochastic optimization so that the solution found is dependent on the set of random variables generated.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 76 pp. Englisch.

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Artificial neural networks are complex networks emulating the manner human rational neurons process data. They have been widely used in prediction, clustering, classification, and association. The training algorithms that determine the network weights are almost the most important factor that influences the neural network's performance. Lately several meta-heuristic and Evolutionary algorithms are employed to optimize neural networks weights to realize higher neural performance.To solve complex computational problems many meta-heuristic optimization algorithms have been developed. A meta-heuristic is a higher-level procedure designed to discover, create, or select a heuristic that may provide a sufficiently good solution to an optimization problem, especially with incomplete or imperfect information or limited computation capacity. Meta-heuristics may make limited assumptions about the optimization problem being solved, and so they may be usable for a variety of problems. Many meta-heuristics implement some form of stochastic optimization so that the solution found is dependent on the set of random variables generated.