On fuzzy modeling using fuzzy neural networks with the back-propagation algorithm

1992 ◽  
Vol 3 (5) ◽  
pp. 801-806 ◽  
Author(s):  
S.-i. Horikawa ◽  
T. Furuhashi ◽  
Y. Uchikawa
Keyword(s):  
Neural Networks ◽  
Back Propagation ◽  
Fuzzy Modeling ◽  
Fuzzy Neural Networks ◽  
Back Propagation Algorithm ◽  
Propagation Algorithm ◽  
Fuzzy Neural

Artificial neural networks back propagation algorithm for cutting force components predictions

2013 ◽  
Vol 14 (6) ◽  
pp. 431-439 ◽  
Author(s):  
Issam Hanafi ◽  
Francisco Mata Cabrera ◽  
Abdellatif Khamlichi ◽  
Ignacio Garrido ◽  
José Tejero Manzanares
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Cutting Force ◽  
Back Propagation ◽  
Back Propagation Algorithm ◽  
Propagation Algorithm ◽  
Cutting Force Components ◽  
Artificial Neural ◽  
Force Components

An Investigation of Trained Neural Networks from a Neurophysiological Perspective

Perception ◽  
1989 ◽  
Vol 18 (6) ◽  
pp. 793-803 ◽  
Author(s):  
Ian R Moorhead ◽  
Nigel D Haig ◽  
Richard A Clement
Keyword(s):  
Neural Networks ◽  
Receptive Field ◽  
Direct Relationship ◽  
Back Propagation ◽  
Receptive Fields ◽  
Recognition System ◽  
Output Function ◽  
Back Propagation Algorithm ◽  
Propagation Algorithm ◽  
The Neural Networks

The application of theoretical neural networks to preprocessed images was investigated with the aim of developing a computational recognition system. The neural networks were trained by means of a back-propagation algorithm, to respond selectively to computer-generated bars and edges. The receptive fields of the trained networks were then mapped, in terms of both their synaptic weights and their responses to spot stimuli. There was a direct relationship between the pattern of weights on the inputs to the hidden units (the units in the intermediate layer between the input and the output units), and their receptive field as mapped by spot stimuli. This relationship was not sustained at the level of the output units in that their spot-mapped responses failed to correspond either with the weights of the connections from the hidden units to the output units, or with a qualitative analysis of the networks. Part of this discrepancy may be ascribed to the output function used in the back-propagation algorithm.

scholarly journals Automatic Fuzzy Modeling for Ginjo Sake Brewing Process using Fuzzy Neural Networks.

1997 ◽  
Vol 30 (1) ◽  
pp. 94-100 ◽  
Author(s):  
Taizo Hanai ◽  
Akemi Katayama ◽  
Hiroyuki Honda ◽  
Takeshi Kobayashi
Keyword(s):  
Neural Networks ◽  
Fuzzy Modeling ◽  
Fuzzy Neural Networks ◽  
Fuzzy Neural ◽  
Brewing Process

scholarly journals Building robust neural networks using different loss functions

2021 ◽  
pp. 67-82
Author(s):  
Maria Sivak ◽  
◽  
Vladimir Timofeev ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Loss Function ◽  
Back Propagation ◽  
Loss Functions ◽  
Back Propagation Algorithm ◽  
Error Back Propagation ◽  
Propagation Algorithm ◽  
Error Back Propagation Algorithm ◽  
Parameter Values

The paper considers the problem of building robust neural networks using different robust loss functions. Applying such neural networks is reasonably when working with noisy data, and it can serve as an alternative to data preprocessing and to making neural network architecture more complex. In order to work adequately, the error back-propagation algorithm requires a loss function to be continuously or two-times differentiable. According to this requirement, two five robust loss functions were chosen (Andrews, Welsch, Huber, Ramsey and Fair). Using the above-mentioned functions in the error back-propagation algorithm instead of the quadratic one allows obtaining an entirely new class of neural networks. For investigating the properties of the built networks a number of computational experiments were carried out. Different values of outliers’ fraction and various numbers of epochs were considered. The first step included adjusting the obtained neural networks, which lead to choosing such values of internal loss function parameters that resulted in achieving the highest accuracy of a neural network. To determine the ranges of parameter values, a preliminary study was pursued. The results of the first stage allowed giving recommendations on choosing the best parameter values for each of the loss functions under study. The second stage dealt with comparing the investigated robust networks with each other and with the classical one. The analysis of the results shows that using the robust technique leads to a significant increase in neural network accuracy and in a learning rate.

ALGORITMA PROPAGASI BALIK DALAM PENCARIAN POLA TRAINING TERBAIK UNTUK MENENTUKAN PREDIKSI PRODUKSI USAHA SONGKET SILUNGKANG DENGAN MENGGUNAKAN MATLAB

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Rima Liana Gema ◽  
Devia Kartika
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Artificial Neural Networks ◽  
Back Propagation ◽  
Back Propagation Algorithm ◽  
Backpropagation Algorithm ◽  
Propagation Algorithm ◽  
Testing Stage ◽  
West Sumatra

One method used in Artificial Neural Networks is a backpropagation algorithm that is widely used in predicting and pattern recognition. Songket is one of the works of skilled hands of the original Silungkang craftsmen, Sawahlunto City, West Sumatra who have varied and unique patterns and motifs. This study uses a back propagation algorithm to find the best training pattern to facilitate the determination of the production prediction of Silungkang songket business using the Matlab application. The best training patterns obtained are expected to be used in data processing at the testing stage in order to obtain predictions for the production of songket business for the future. Keywords: production, songket, back propagation.

Artificial Neural Network Modelling for Waste

2011 ◽  
pp. 224-263 ◽  
Author(s):  
Eldon R. Rene ◽  
M. Estefanía López ◽  
María C. Veiga ◽  
Christian Kennes
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Wastewater Treatment ◽  
Back Propagation ◽  
Back Propagation Algorithm ◽  
Network Modelling ◽  
Waste Gas ◽  
Propagation Algorithm ◽  
Artificial Neural

Due to their inherent robustness, artificial neural network models have proven to be successful and have been used extensively in biological wastewater treatment applications. However, only recently, with the scientific advancements made in biological waste gas treatment systems, the application of neural networks have slowly gained the practical momentum for performance monitoring in this field. Simple neural models, after vigorous training and testing, are able to generalize the results of a wide range of operating conditions, with high prediction accuracy. This chapter gives a fundamental insight and overview of the process mechanism of different biological waste gas (biofilters, biotrickling filters, continuous stirred tank bioreactors and monolith bioreactors), and wastewater treatment systems (activated sludge process, trickling filter and sequencing batch reactors). The basic theory of artificial neural networks is explained with a clear understanding of the back propagation algorithm. A generalized neural network modelling procedure for waste treatment applications is outlined, and the role of back propagation algorithm network parameters is discussed. Anew, the application of neural networks for solving specific environmental problems is presented in the form of a literature review.

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