23,99 €
inkl. MwSt.
Versandfertig in 6-10 Tagen
12 °P sammeln
Andere Kunden interessierten sich auch für
![Fast Decision Tree To Index Large DNA-Protein Sequence Datasets]( "Fast Decision Tree To Index Large DNA-Protein Sequence Datasets")
Fast Decision Tree To Index Large DNA-Protein Sequence Datasets51,99 €![Handbook of swarm intelligence: algorithms and applications]( "Handbook of swarm intelligence: algorithms and applications")
Handbook of swarm intelligence: algorithms and applications42,99 €![Evolutionary Multi-objective Optimization]( "Evolutionary Multi-objective Optimization")
Evolutionary Multi-objective Optimization95,99 €![Searching in non-overlapping Neighbor ranges on Plane Sweep Algorithm]( "Searching in non-overlapping Neighbor ranges on Plane Sweep Algorithm")
Searching in non-overlapping Neighbor ranges on Plane Sweep Algorithm23,99 €![Speeding Up Distributed Constraint Optimization Search Algorithms]( "Speeding Up Distributed Constraint Optimization Search Algorithms")
Speeding Up Distributed Constraint Optimization Search Algorithms51,99 €![The Multi-Criteria Ranking Approach to Classification Algorithms]( "The Multi-Criteria Ranking Approach to Classification Algorithms")
The Multi-Criteria Ranking Approach to Classification Algorithms26,99 €![Multi HLR Architecture For Location Management in PCS Network]( "Multi HLR Architecture For Location Management in PCS Network")
Multi HLR Architecture For Location Management in PCS Network26,99 €
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 resultshave 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.