Application of Multilayer Perceptron and Radial Basis Function Neural Network in steady state modeling of automotive air conditioning system

As Redes Neurais Artificiais tem-se destacado na resolução de problemas em diversas áreas. Nesse sentido realizou-se um estudo com a implementação e análise das redes Multilayer Perceptron (MLP) e Radial Basis Function Neural Network (RBF), objetivando comparar resultados baseados no treinamento, teste e classificação de crianças com ou sem autismo. A metodologia foi implementada com base em 292 amostras de indivíduos de um banco de dados público, através da ferramenta Matlab R2015a, dividas em 10 partes com validação cruzada. Os resultados foram analisados considerando as características e os comportamentos diferentes das redes implementadas, obtendo-se uma medida da qualidade atingida.

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Multilayer Perceptron and Evolutionary Radial Basis Function Neural Network Models for Discrimination of HIV-1 Genomes

Current Science ◽

10.18520/cs/v115/i11/2063-2070 ◽

2018 ◽

Vol 115 (11) ◽

pp. 2063 ◽

Cited By ~ 2

Author(s):

Ashok Kumar Dwivedi ◽

Usha Chouhan

Keyword(s):

Neural Network ◽

Radial Basis Function ◽

Basis Function ◽

Multilayer Perceptron ◽

Network Models ◽

Neural Network Models ◽

Radial Basis ◽

Hiv 1

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Menentukan Batas Kestabilan Steady State Sistem Jawa Bali 500 kV menggunakan Pendekatan Model Single Machine to Infinite Bus

ALINIER: Journal of Artificial Intelligence & Applications ◽

10.36040/alinier.v1i1.2517 ◽

2020 ◽

Vol 1 (1) ◽

pp. 1-12

Author(s):

Rusilawati

Keyword(s):

Neural Network ◽

Steady State ◽

Radial Basis Function ◽

Single Machine ◽

Basis Function ◽

Stability Limit ◽

Radial Basis ◽

Generator Unit ◽

Configuration Changes ◽

Steady State Stability

The indicator of the power system operation stability can be seen from the power balance between the load demand and the generator output power. The Single Machine to Infinite Bus (SMIB) system that can actually represent the operation of a single machine system in a multimachine system can be used to analyze each generator unit stability. This paper present a fairly simple method to determine the generator steady state stability limit on the Jawa Bali 500 kV system using an SMIB system approach consider the load configuration changes in the system. The Radial Basis Function Neural Network (RBFNN) is applied to simplify the determination of the generator steady state stability limit that changes every time a load configuration changes. The simulation results carried out on the Java Bali system 500 kV 29 bus 10 generators can be seen that the steady state stability limit of each generator unit tends to decrease with the increasing of loading value and the further of load distance from the generator. Keseimbangan daya antara kebutuhan beban dengan pembangkitan generator merupakan salah satu ukuran kestabilan operasi sistem tenaga listrik., Untuk menganalisis kestabilan setiap unit generator dalam sistem multimachine harus dilakukan pada sistem Single Machine to Infinite Bus (SMIB) yang secara aktual dapat mewakili keadaan sistem single machine tersebut dalam sebuah sistem multimachine. Dalam paper ini digunakan suatu metode sederhana untuk menentukan batas kestabilan steady state setiap unit generator pada sistem multimachine Jawa Bali 500 kV menggunakan pendekatan model sistem SMIB dengan memperhatikan perubahan konfigurasi peletakan beban dalam sistem. Untuk memudahkan penentuan batas kestabilan steady state generator yang selalu berubah setiap saat terjadi perubahan peletakan beban, diaplikasikan salah satu model jaring syaraf tiruan yaitu Radial Basis Function Neural Network (RBFNN). Dari hasil simulasi yang dilakukan pada sistem Jawa Bali 500 kV 29 bus 10 generator dapat diketahui bahwa batas kestabilan steady state setiap unit generator cenderung menurun dengan semakin meningkatnya nilai pembebanan dan semakin jauhnya jarak beban dari pembangkit.

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