CFP last date

by
Benuwa Ben-Bright,
Yongzhao Zhan,
Benjamin Ghansah,
Richard Amankwah,
Dickson Keddy Wornyo,
Ernest Ansah

International Journal of Computer Applications |

Foundation of Computer Science (FCS), NY, USA |

Volume 163 - Number 4 |

Year of Publication: 2017 |

Authors: Benuwa Ben-Bright, Yongzhao Zhan, Benjamin Ghansah, Richard Amankwah, Dickson Keddy Wornyo, Ernest Ansah |

10.5120/ijca2017913513 |

Benuwa Ben-Bright, Yongzhao Zhan, Benjamin Ghansah, Richard Amankwah, Dickson Keddy Wornyo, Ernest Ansah . Taxonomy and a Theoretical Model for Feedforward Neural Networks. International Journal of Computer Applications. 163, 4 ( Apr 2017), 39-49. DOI=10.5120/ijca2017913513

@article{
10.5120/ijca2017913513,

author = {
Benuwa Ben-Bright,
Yongzhao Zhan,
Benjamin Ghansah,
Richard Amankwah,
Dickson Keddy Wornyo,
Ernest Ansah
},

title = { Taxonomy and a Theoretical Model for Feedforward Neural Networks },

journal = {
International Journal of Computer Applications
},

issue_date = { Apr 2017 },

volume = { 163 },

number = { 4 },

month = { Apr },

year = { 2017 },

issn = { 0975-8887 },

pages = {
39-49
},

numpages = {9},

url = {
https://ijcaonline.org/archives/volume163/number4/27386-2017913513/
},

doi = { 10.5120/ijca2017913513 },

publisher = {Foundation of Computer Science (FCS), NY, USA},

address = {New York, USA}

}

%0 Journal Article

%1 2024-02-07T00:09:17.322997+05:30

%A Benuwa Ben-Bright

%A Yongzhao Zhan

%A Benjamin Ghansah

%A Richard Amankwah

%A Dickson Keddy Wornyo

%A Ernest Ansah

%T Taxonomy and a Theoretical Model for Feedforward Neural Networks

%J International Journal of Computer Applications

%@ 0975-8887

%V 163

%N 4

%P 39-49

%D 2017

%I Foundation of Computer Science (FCS), NY, USA

Feedforward Neural Network (FFNN) is a surrogate of Artificial Neural Network (ANN) in which links amongst the units do not form a directed cycle. ANNs, akin to the vast network of neurons in the brain (human central nervous system) are usually presented as systems of interweaving connected "neurons" which exchange messages between each other. These connections have numeric hefts that can be adjusted and grounded on experience, enforcing adaptively on neural networks to inputs and learning capabilities. This paper presents a comprehensive review of FFNN with emphasis on implantation issues, which have been addressed by previous approaches. We also propose a theoretical model that exhibits potential superior performances in terms of convergence speed, efficient and effective computation and generality than state of the art models.

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