Application of Feed-Forward Neural Networks for Classifying Acoustics Levels in Vehicle Cabin

2013 ◽  
Vol 471 ◽  
pp. 40-44 ◽  
Author(s):  
Ahmad Kadri Junoh ◽  
Zulkifli Mohd Nopiah ◽  
Ahmad Kamal Ariffin
Keyword(s):  
Neural Network ◽  
Engine Speed ◽  
Optimization Approach ◽  
Feed Forward Neural Network ◽  
Neural Network Optimization ◽  
Noise Pattern ◽  
Feed Forward ◽  
Vehicle Cabin ◽  
Feed Forward Neural Networks ◽  
Two Phases

Vehicle acoustical comfort and vibration in a passenger car cabin are the main factors that attract a buyer in car purchase. Numerous studies have been carried out by automotive researchers to identify and classify the acoustics level in the vehicle cabin. The objective is to form a special benchmark for acoustics level that may be referred for any acoustics improvement purpose. This study is focused on the sound quality change over the engine speed [rp to recognize the noise pattern experienced in the vehicle cabin. Since it is difficult for a passenger to express, and to evaluate the noise experienced or heard in a numerical scale, a neural network optimization approach is used to classify the acoustics levels into groups of noise annoyance levels. A feed forward neural network technique is applied for classification algorithm, where it can be divided into two phases: Learning Phase and Classification Phase. The developed model is able to classify the acoustics level into numerical scales which are meaningful for evaluation purposes.

Missing Data Approaches to Classification

2010 ◽  
pp. 187-209
Author(s):  
Tshilidzi Marwala
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Missing Data ◽  
Network Model ◽  
Network Models ◽  
Feed Forward Neural Network ◽  
Neural Network Models ◽  
Feed Forward ◽  
Missing Data Problem ◽  
Feed Forward Neural Networks

In this chapter, a classifier technique that is based on a missing data estimation framework that uses autoassociative multi-layer perceptron neural networks and genetic algorithms is proposed. The proposed method is tested on a set of demographic properties of individuals obtained from the South African antenatal survey and compared to conventional feed-forward neural networks. The missing data approach based on the autoassociative network model proposed gives an accuracy of 92%, when compared to the accuracy of 84% obtained from the conventional feed-forward neural network models. The area under the receiver operating characteristics curve for the proposed autoassociative network model is 0.86 compared to 0.80 for the conventional feed-forward neural network model. The autoassociative network model proposed in this chapter, therefore, outperforms the conventional feed-forward neural network models and is an improved classifier. The reasons for this are: (1) the propagation of errors in the autoassociative network model is more distributed while for a conventional feed-forward network is more concentrated; and (2) there is no causality between the demographic properties and the HIV and, therefore, the HIV status does change the demographic properties and vice versa. Therefore, it is better to treat the problem as a missing data problem rather than a feed-forward problem.

scholarly journals A Neural Network Approximation Based on a Parametric Sigmoidal Function

Mathematics ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 262 ◽  
Author(s):  
Beong Yun
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Approximation Method ◽  
Closed Interval ◽  
Activation Function ◽  
Sigmoidal Function ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
Extended Application ◽  
Feed Forward Neural Networks

It is well known that feed-forward neural networks can be used for approximation to functions based on an appropriate activation function. In this paper, employing a new sigmoidal function with a parameter for an activation function, we consider a constructive feed-forward neural network approximation on a closed interval. The developed approximation method takes a simple form of a superposition of the parametric sigmoidal function. It is shown that the proposed method is very effective in approximation of discontinuous functions as well as continuous ones. For some examples, the availability of the presented method is demonstrated by comparing its numerical results with those of an existing neural network approximation method. Furthermore, the efficiency of the method in extended application to the multivariate function is also illustrated.

scholarly journals Algorithm for calculating synapse weights of the first layer of a neural network on the base of metric recognition methods. Part 1.

2020 ◽  
pp. 20-30
Author(s):  
Polad Geidarov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Three Dimensional ◽  
Analytical Calculation ◽  
Training Algorithms ◽  
Feed Forward Neural Network ◽  
Software Environment ◽  
Feed Forward ◽  
Feed Forward Neural Networks ◽  
And Training

Introduction: Metric recognition methods make it possible to preliminarily and strictly determine the structures of feed-forward neural networks, namely, the number of neurons, layers, and connections based on the initial parameters of the recognition problem. They also make it possible to analytically calculate the synapse weights of network neurons based on metric expressions. The setup procedure for these networks includes a sequential analytical calculation of the values of each synapse weight in the weight table for neurons of the zero or first layer, which allows us to obtain a working feed-forward neural network at the initial stage without the use of training algorithms. Then feed-forward neural networks can be trained by well-known learning algorithms, which generally speeds up the process of their creation and training. Purpose: To determine how much time the process of calculating the values of weights requires and, accordingly, how reasonable it is to preliminarily calculate the weights of a feed-forward neural network. Results: An algorithm is proposed and implemented for the automated calculation of all values of synapse weight tables for the zero and first layers as applied to the task of recognizing black-and-white monochrome symbol images. The proposed algorithm is described in the Builder C++ software environment. The possibility of optimizing the process of calculating the weights of synapses in order to accelerate the entire algorithm is considered. The time spent on calculating these weights for different configurations of neural networks based on metric recognition methods is estimated. Examples of creating and calculating synapse weight tables according to the considered algorithm are given. According to them, the analytical calculation of the weights of a neural network takes just seconds or minutes, being in no way comparable to the time necessary for training a neural network. Practical relevance: Analytical calculation of the weights of a neural network can significantly accelerate the process of creating and training a feed-forward neural network. Based on the proposed algorithm, we can implement one for calculating three-dimensional weight tables for more complex images, either blackand-white or color grayscale ones.

scholarly journals Algorithm for calculating synapse weights of the first layer of a neural network on the base of metric recognition methods. Part 2

2020 ◽  
pp. 25-38
Author(s):  
Polad Geidarov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Analytical Calculation ◽  
Training Algorithms ◽  
Feed Forward Neural Network ◽  
Software Environment ◽  
Feed Forward ◽  
Black And White ◽  
Feed Forward Neural Networks ◽  
And Training

Introduction: Metric recognition methods make it possible to preliminarily and strictly determine the structures of feed-forward neural networks, namely, the number of neurons, layers, and connections based on the initial parameters of the recognition problem. They also make it possible to analytically calculate the synapse weights of network neurons based on metric expressions. The setup procedure for these networks includes a sequential analytical calculation of the values of each synapse weight in the weight table for neurons of the zero or first layer, which allows us to obtain a working feed-forward neural network at the initial stage without the use of training algorithms. Then feed-forward neural networks can be trained by well-known learning algorithms, which generally speeds up the process of their creation and training. Purpose: To determine how much time the process of calculating the values of weights requires and, accordingly, how reasonable it is to preliminarily calculate the weights of a feed-forward neural network. Results: An algorithm is proposed and implemented for the automated calculation of all values of synapse weight tables for the zero and first layers as applied to the task of recognizing black-and-white monochrome symbol images. The proposed algorithm is described in the Builder C++ software environment. The possibility of optimizing the process of calculating the weights of synapses in order to accelerate the entire algorithm is considered. The time spent on calculating these weights for different configurations of neural networks based on metric recognition methods is estimated. Examples of creating and calculating synapse weight tables according to the considered algorithm are given. According to them, the analytical calculation of the weights of a neural network takes just seconds or minutes, being in no way comparable to the time necessary for training a neural network. Practical relevance: Analytical calculation of the weights of a neural network can significantly accelerate the process of creating and training a feed-forward neural network. Based on the proposed algorithm, we can implement one for calculating three-dimensional weight tables for more complex images, either black and-white or color grayscale ones.

scholarly journals Using A Feed Forward Neural Network Algorithm to Predict Prices of Multiple Cryptocurrencies

2021 ◽  
Vol 6 (5) ◽  
pp. 15-19
Author(s):  
Sina E. Charandabi ◽  
Kamyar Kamyar
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Learning Model ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
Price Data ◽  
Machine Learning Model ◽  
Feed Forward Neural Networks ◽  
Neural Network Algorithm

This paper initially presents a nontechnical overview of cryptocurrency, its history, and the technicalities of its usage as a means of exchange. Bitcoin’s working methodology and mathematical baseline is further presented in more depth. For the remaining majority of the paper, recent cryptocurrency price data of Bitcoin, Ethereum, Tether, Dogecoin, and Binance coin was used to train a machine learning model of Feed Forward Neural Networks to predict future prices for each of the datasets. Further and in conclusion, the results are discussed, and the efficiency and accuracy of these models are evaluated.

Evapotranspiration estimation using feed-forward neural networks

2006 ◽  
Vol 37 (3) ◽  
pp. 247-260 ◽  
Author(s):  
Özgür Kişi
Keyword(s):  
Neural Network ◽  
Climatic Data ◽  
Training Algorithms ◽  
Feed Forward Neural Network ◽  
Computing Technique ◽  
Feed Forward ◽  
Feed Forward Neural Networks ◽  
Ann Models ◽  
Artificial Neural Network Ann ◽  
Evapotranspiration Estimation

Two different feed-forward neural network algorithms, Levenberg–Marquardt (LM) and conjugate gradient (CG), are used for estimation of daily reference evapotranspiration (ET) from climatic data. The performances of the LM and CG algorithms in estimating ET are analyzed and discussed and various combinations of wind speed, solar radiation, relative humidity, air and soil temperature data as inputs to the artificial neural network (ANN) models are examined in the study so as to evaluate the degree of the effect of each of these variables on ET. The LM and CG training algorithms are compared with each other according to their convergence velocities in training and estimation performances of ET. The results of the ANN models are compared with those of multi-linear regression (MLR) and the empirical models of Penman and Hargreaves. Based on the comparisons, it was found that the neural computing technique could be employed successfully in modelling evapotranspiration process from the avaliable climatic data.

scholarly journals Feed-Forward Neural Networks for Failure Mechanics Problems

2021 ◽  
Vol 11 (14) ◽  
pp. 6483
Author(s):  
Fadi Aldakheel ◽  
Ramish Satari ◽  
Peter Wriggers
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fracture Behavior ◽  
Research Field ◽  
Failure Model ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
Field Modeling ◽  
Feed Forward Neural Networks ◽  
Active Research

This work addresses an efficient neural network (NN) representation for the phase-field modeling of isotropic brittle fracture. In recent years, data-driven approaches, such as neural networks, have become an active research field in mechanics. In this contribution, deep neural networks—in particular, the feed-forward neural network (FFNN)—are utilized directly for the development of the failure model. The verification and generalization of the trained models for elasticity as well as fracture behavior are investigated by several representative numerical examples under different loading conditions. As an outcome, promising results close to the exact solutions are produced.

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