9783330332683 - Multi- Objective Evolutionary Algorithms of Spiking Neural Network de Saleh, Abdulrazak Yahya (10 resultados)

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Taschenbuch. Condición: Neu. Multi- Objective Evolutionary Algorithms of Spiking Neural Network | Abdulrazak Yahya Saleh | Taschenbuch | 64 S. | Englisch | 2017 | LAP LAMBERT Academic Publishing | EAN 9783330332683 | Verantwortliche Person für die EU: OmniScriptum GmbH & Co. KG, Bahnhofstr. 28, 66111 Saarbrücken, info[at]akademikerverlag[dot]de | Anbieter: preigu.

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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Spiking neural network (SNN) plays an essential role in classification problems. Although there are many models of SNN, Evolving Spiking Neural Network (ESNN) is widely used in many recent research works. Evolutionary algorithms, mainly differential evolution (DE) have been used for enhancing ESNN algorithm. However, many real-world optimisation problems include several contradictory objectives. Rather than single optimisation, Multi-Objective Optimisation (MOO) can be utilised as a set of optimal solutions to solve these problems.In this book, Harmony Search (HS) and memetic approach were used to improve the performance of MOO with ESNN. Consequently, Memetic Harmony Search Multi-Objective Differential Evolution with Evolving Spiking Neural Network (MEHSMODE-ESNN) was applied to improve ESNN structure and accuracy rates. Standard data sets from the UCI machine learning are used for evaluating the performance of this enhanced multi objective hybrid model. The experimental results have proved that the Memetic Harmony Search Multi-Objective Differential Evolution with Evolving Spiking Neural Network (MEHSMODE-ESNN) gives better results in terms of accuracy and network structure. 64 pp. Englisch.

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Saleh Abdulrazak YahyaReceived MSc in Computer Science at the UST and PhD in Computer Science (Artificial Intelligence) at the University Teknologi Malaysia (UTM). He is interested in cognitive science, brain modelling, spiking neura.

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Spiking neural network (SNN) plays an essential role in classification problems. Although there are many models of SNN, Evolving Spiking Neural Network (ESNN) is widely used in many recent research works. Evolutionary algorithms, mainly differential evolution (DE) have been used for enhancing ESNN algorithm. However, many real-world optimisation problems include several contradictory objectives. Rather than single optimisation, Multi-Objective Optimisation (MOO) can be utilised as a set of optimal solutions to solve these problems.In this book, Harmony Search (HS) and memetic approach were used to improve the performance of MOO with ESNN. Consequently, Memetic Harmony Search Multi-Objective Differential Evolution with Evolving Spiking Neural Network (MEHSMODE-ESNN) was applied to improve ESNN structure and accuracy rates. Standard data sets from the UCI machine learning are used for evaluating the performance of this enhanced multi objective hybrid model. The experimental results have proved that the Memetic Harmony Search Multi-Objective Differential Evolution with Evolving Spiking Neural Network (MEHSMODE-ESNN) gives better results in terms of accuracy and network structure.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 64 pp. Englisch.

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Spiking neural network (SNN) plays an essential role in classification problems. Although there are many models of SNN, Evolving Spiking Neural Network (ESNN) is widely used in many recent research works. Evolutionary algorithms, mainly differential evolution (DE) have been used for enhancing ESNN algorithm. However, many real-world optimisation problems include several contradictory objectives. Rather than single optimisation, Multi-Objective Optimisation (MOO) can be utilised as a set of optimal solutions to solve these problems.In this book, Harmony Search (HS) and memetic approach were used to improve the performance of MOO with ESNN. Consequently, Memetic Harmony Search Multi-Objective Differential Evolution with Evolving Spiking Neural Network (MEHSMODE-ESNN) was applied to improve ESNN structure and accuracy rates. Standard data sets from the UCI machine learning are used for evaluating the performance of this enhanced multi objective hybrid model. The experimental results have proved that the Memetic Harmony Search Multi-Objective Differential Evolution with Evolving Spiking Neural Network (MEHSMODE-ESNN) gives better results in terms of accuracy and network structure.