Novel Neuronal Activation Functions for Feedforward Neural Networks

Efe, Mehmet Önder

doi:10.1007/s11063-008-9082-0

Cited by 15 publications

(12 citation statements)

References 14 publications

(24 reference statements)

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“…The activation function is used to calculate the output response of neuron in which the sum of the weighted input signal is applied with an activation to obtain the response. Different activation or transfer functions have been proposed by different scholars and researchers satisfactory results such as Lorentzian transfer functions [128], Max-Piecewise-Linear (MPWL) Neural Network for function approximation [221], non-polynomial activation functions [33], Hermite Polynomial [132], Gaussian bars [85], hybridization of various functions such as polynomial, periodic, sigmoidal and Gaussian functions [159], two new activation functions labelled sincos and sinc [63], Hybridization of used complementary log-log and probit functions [78]. Giraud in [77] proposed a new class of sigmoidal functions which has proven satisfied with universal approximation theorem requirement.…”

Section: Neural Network (Nn)mentioning

confidence: 99%

A Holistic Review of Soft Computing Techniques

Philip¹,

Mom²,

Igwue³

2017

ACM

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Due to notable technological convergence that brought about exponential growth in computer world, SoftComputing (SC) has played a vital role with automation capability features to new levels of complex applications. In this research paper, the authors reviewed journals related to the subject matter with the aim of striking a convincing balance between a system that is capable of tolerance to uncertainty, imprecision, approximate reasoning and partial truth to achieve tractability, robustness, economy of communication, high machine intelligence quotient (MIQ), low cost solution and better rapport with reality to conventional techniques. This paper gives an insight on four major consortiums of SC that sprang from the concept of cybernetics, explores and reviews the different techniques, methodologies; application areas and algorithms are formulated to give an idea on how these computing techniques are applied to create intelligent agents to solve a variety of problems. The mechanisms highlighted can serve as an inspiration platform and awareness to new and old researchers that are not or fully grounded in this unique area of research and to create avenue in order to fully embrace the techniques in research communities.

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Section: Neural Network (Nn)mentioning

confidence: 99%

A Holistic Review of Soft Computing Techniques

Philip¹,

Mom²,

Igwue³

2017

ACM

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“…Another work investigating an activation function based on sinusoidal modulation can be found in [25], where the authors propose a cosine modulated Gaussian function. The use of sinusoidal activation function is deeply investigated in [26], where the authors present a comprehensive comparison between eight different activation functions on eight different problems. Among other results, the Sinc activation function is proved as a valid alternative to the hyperbolic tangent, and the sinusoidal activation function has good training performance on small FFNNs.…”

Section: Activation Functions For Easy Trainingmentioning

confidence: 99%

On Training Efficiency and Computational Costs of a Feed Forward Neural Network: A Review

Laudani

Lozito

Fulginei

et al. 2015

Computational Intelligence and Neuroscience

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A comprehensive review on the problem of choosing a suitable activation function for the hidden layer of a feed forward neural network has been widely investigated. Since the nonlinear component of a neural network is the main contributor to the network mapping capabilities, the different choices that may lead to enhanced performances, in terms of training, generalization, or computational costs, are analyzed, both in general-purpose and in embedded computing environments. Finally, a strategy to convert a network configuration between different activation functions without altering the network mapping capabilities will be presented.

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“…Further training of the Error Back Propagation Artificial Neural Network is continued till the desired classification performance is reached. The algorithm consist of two parts first part is forward propagation, second part is reverse propagation which explain below the two parts and Multilayer feedforward networks are trained using Error back propagation Artificial Neural Network learning algorithm as shown in Figure (2) [7].…”

Section: Error Back Propagation Artificial Neural Networkmentioning

confidence: 99%

High Accuracy Arabic Handwritten Characters Recognition Using Error Back Propagation Artificial Neural Networks

Abed¹,

Alasad²

2015

ijacsa

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Abstract-This manuscript considers a new architecture to handwritten characters recognition based on simulation of the behavior of one type of artificial neural network, called the Error Back Propagation Artificial Neural Network (EBPANN). We present an overview of our neural network to be optimized and tested on 12 offline isolated Arabic handwritten characters ‫،ش،ط،غ،ف،ك،م،و،ي(‬ ‫)ا,ت,ج،ذ‬ because the similarity of some Arabic characters and the location of the points in the character. Accuracy of 93.61% is achieved using EBPANN which is the highest accuracy achieved during Offline Handwritten Arabic Character Recognition. It is noted that the EBPANN in general generates an optimized comparison between the input samples and database samples which improves the final recognition rate. Experimental results show that the EBPANN is convergent and more accurate in solutions that minimize the error recognition rate.

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Novel Neuronal Activation Functions for Feedforward Neural Networks

Cited by 15 publications

References 14 publications

A Holistic Review of Soft Computing Techniques

A Holistic Review of Soft Computing Techniques

On Training Efficiency and Computational Costs of a Feed Forward Neural Network: A Review

High Accuracy Arabic Handwritten Characters Recognition Using Error Back Propagation Artificial Neural Networks

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