Improved SpikeProp for Using Particle Swarm Optimization

Ahmed, Falah Y. H.; Shamsuddin, Siti Mariyam; Hashim, Siti Zaiton Mohd

doi:10.1155/2013/257085

Cited by 16 publications

(12 citation statements)

References 21 publications

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“…1 depicts a simplified architecture of ESNN model which was explained in more detail in [3]. The training starts with initialization of three ESNN parameters -modulation factor (Mod), proportion factor (C) and similarity value (Sim) in the interval [0,1]. Mod is the modulation factor of the Thorpe neural model.…”

Section: Evolving Spiking Neural Network ( Esnn)mentioning

confidence: 99%

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Multi-Objective Differential Evolution of Evolving Spiking Neural Networks for Classification Problems

Saleh¹,

Shamsuddin²,

Hamed³

2015

IFIP Advances in Information and Communication Technology

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Section: Evolving Spiking Neural Network ( Esnn)mentioning

confidence: 99%

“…Classification of patterns is vital to several data mining processes. Classification is one of the most commonly obverse processing tasks for a decision support system [1]. There are many areas in life which need classification such as medical diagnoses, medicine, science, industry, speech recognition and handwritten character recognition.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Differential Evolution of Evolving Spiking Neural Networks for Classification Problems

Saleh¹,

Shamsuddin²,

Hamed³

2015

IFIP Advances in Information and Communication Technology

Self Cite

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show abstract

“…The training starts with initialization of three ESNN parameters -modulation factor (Mod), proportion factor (C) and similarity value (Sim) in the interval [0,1]. Mod is the modulation factor of the Thorpe neural model.…”

Section: Evolving Spiking Neural Network (Esnn)mentioning

confidence: 99%

“…Classification is one of the most commonly obverse processing tasks for a decision support system [1]. There are many areas in life which need classification such as medical diagnoses, medicine, science, industry, speech recognition and handwritten character recognition.…”

Section: Introductionmentioning

confidence: 99%

Memetic Harmony Search Algorithm Based on Multi-objective Differ-ential Evolution of Evolving Spiking Neural Networks

Saleh¹,

Shamsuddin²

2015

Int J Swarm Intel Evol Comput

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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 realworld optimization problems include several contradictory objectives. Rather than single optimization, Multi-Objective Optimization (MOO) can be utilized as a set of optimal solutions to solve these problems. In this paper, Harmony Search (HS) and memetic approach was 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. Unlike previous study mentioned earlier in Jin [14] and other single objective studies [18,19], this paper deals with an improved multi objective method to obtain simple and accurate ESNN. The proposed method evolves toward optimal values defined by several objectives with model accuracy and ESNN's structure to improve performance for classification problems. The remaining parts of this paper are organized as follows: Methods including: Evolving Spiking Neural Network, Multi Objective Differential Evolution are presented in section 2. In addition, section 3 clarifies the proposed method Memetic Harmony Search Multi-Objective Differential Evolution with Evolving Spiking Neural Network (MEHSMODE-ESNN) used in this paper, Experimental design is discussed in section 4, section 5 explains in detail the results and discussion, and finally, section 6 concludes the paper with future works. MethodsThis section reviews the vital foundation of evolving spiking neural network (ESNN) and discusses the evolutionary algorithms that have been utilized for enhancement. In the first part, an introduction of ESNN, neuron coding, learning method and ESNN design, and its algorithm is presented. The second part focuses on the algorithms which are used for improvement of ESNN. The concepts and methods of multi objective optimization (MOO) are highlighted. After that, the literature focuses on the working of differential evolution, harmony search (HS) algorithm and memetic approach which is used to improve and enhance the performance of classification . Evolving spiking neural network (ESNN)Currently, several enhancements of SNN have been proposed. Wysoski improved one of these new models known as Evolving Spiking Neural Network (ESNN) [20]. Generally, ESNN used the principles of evolving connectionist systems (ECOS) where neurons are created incrementall...

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“…PSO has attracted increasing attention because of its simple concept, easy implementation, and quick. These advantages allow it to be applied successfully in a variety of fields, mainly for unconstrained continuous optimization problems [19][20][21][22][23][24][25][26]. Therefore, in this work, we explore the PSO algorithm with certain modification based on particle fitness evaluation to solve the problem (15).…”

Section: Probability Modifying Factors Optimizationmentioning

confidence: 99%

A Generalization Belief Propagation Decoding Algorithm for Polar Codes Based on Particle Swarm Optimization

Zhang

Liu

Pan

et al. 2014

Mathematical Problems in Engineering

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We propose a generalization belief propagation (BP) decoding algorithm based on particle swarm optimization (PSO) to improve the performance of the polar codes. Through the analysis of the existing BP decoding algorithm, we first introduce a probability modifying factor to each node of the BP decoder, so as to enhance the error correcting capacity of the decoding. Then, we generalize the BP decoding algorithm based on these modifying factors and drive the probability update equations for the proposed decoding. Based on the new probability update equations, we show the intrinsic relationship of the existing decoding algorithms. Finally, in order to achieve the best performance, we formulate an optimization problem to find the optimal probability modifying factors for the proposed decoding algorithm. Furthermore, a method based on the modified PSO algorithm is also introduced to solve that optimization problem. Numerical results show that the proposed generalization BP decoding algorithm achieves better performance than that of the existing BP decoding, which suggests the effectiveness of the proposed decoding algorithm.

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Improved SpikeProp for Using Particle Swarm Optimization

Cited by 16 publications

References 21 publications

Multi-Objective Differential Evolution of Evolving Spiking Neural Networks for Classification Problems

Multi-Objective Differential Evolution of Evolving Spiking Neural Networks for Classification Problems

Memetic Harmony Search Algorithm Based on Multi-objective Differ-ential Evolution of Evolving Spiking Neural Networks

A Generalization Belief Propagation Decoding Algorithm for Polar Codes Based on Particle Swarm Optimization

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