Chebyshev Multilayer Perceptron Neural Network with Levenberg Marquardt-Back Propagation Learning for Classification Tasks

2016 ◽  
pp. 162-170 ◽  
Author(s):  
Umer Iqbal ◽  
Rozaida Ghazali
Keyword(s):  
Neural Network ◽  
Multilayer Perceptron ◽  
Back Propagation ◽  
Levenberg Marquardt ◽  
Classification Tasks

Comparison of an Artificial Neural Network and a Multiple Linear Regression in Predicting the Heat of Combustion of Diesel Fuel Based on Hydrocarbon Groups

2020 ◽  
Vol 71 (6) ◽  
pp. 66-74
Author(s):  
Younis M. Younis ◽  
Salman H. Abbas ◽  
Farqad T. Najim ◽  
Firas Hashim Kamar ◽  
Gheorghe Nechifor
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Diesel Fuel ◽  
Back Propagation ◽  
Second Step ◽  
Heating Value ◽  
Network Training ◽  
Marquardt Algorithm ◽  
Levenberg Marquardt ◽  
Propagation Network

A comparison between artificial neural network (ANN) and multiple linear regression (MLR) models was employed to predict the heat of combustion, and the gross and net heat values, of a diesel fuel engine, based on the chemical composition of the diesel fuel. One hundred and fifty samples of Iraqi diesel provided data from chromatographic analysis. Eight parameters were applied as inputs in order to predict the gross and net heat combustion of the diesel fuel. A trial-and-error method was used to determine the shape of the individual ANN. The results showed that the prediction accuracy of the ANN model was greater than that of the MLR model in predicting the gross heat value. The best neural network for predicting the gross heating value was a back-propagation network (8-8-1), using the Levenberg�Marquardt algorithm for the second step of network training. R = 0.98502 for the test data. In the same way, the best neural network for predicting the net heating value was a back-propagation network (8-5-1), using the Levenberg�Marquardt algorithm for the second step of network training. R = 0.95112 for the test data.

Predicting Seepage of Earth Dams Using Neural Network and Genetic Algorithm

2011 ◽  
Vol 403-408 ◽  
pp. 3081-3085 ◽  
Author(s):  
Xin Ying Miao ◽  
Jin Kui Chu ◽  
Jing Qiao ◽  
Ling Han Zhang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Field Data ◽  
Back Propagation ◽  
Earth Dam ◽  
Earth Dams ◽  
Gradient Descent Algorithm ◽  
Levenberg Marquardt ◽  
Lm Algorithm ◽  
Good Agreement

Measurements of seepage are fundamental for earth dam surveillance. However, it is difficult to establish an effective and practical dam seepage prediction model due to the nonlinearity between seepage and its influencing factors. Genetic Algorithm for Levenberg-Marquardt(GA-LM), a new neural network(NN) model has been developed for predicting the seepage of an earth dam in China using 381 databases of field data (of which 366 in 2008 were used for training and 15 in 2009 for testing). Genetic algorithm(GA) is an ecological system algorithm, which was adopted to optimize the NN structure. Levenberg-Marquardt (LM) algorithm was originally designed to serve as an intermediate optimization algorithm between the Gauss-Newton(GN) method and the gradient descent algorithm, which was used to train NN. The predicted seepage values using GA-LM model are in good agreement with the field data. It is demonstrated here that the model is capable of predicting the seepage of earth dams accurately. The performance of GA-LM has been compared with that of conventional Back-Propagation(BP) algorithm and LM algorithm with trial-and-error approach. The comparison indicates that the GA-LM model can offer stronger and better performance than conventional NNs when used as a quick interpolation and extrapolation tool.

Dual-mode dendritic devices enhanced neural network based on electrolyte gated transistors

2021 ◽  
Author(s):  
Zhaokun Jing ◽  
Yuchao Yang ◽  
Ru Huang
Keyword(s):  
Neural Network ◽  
Multilayer Perceptron ◽  
Back Propagation ◽  
Input Voltage ◽  
High Signal ◽  
Neuron Models ◽  
Dual Mode ◽  
Dendritic Processing ◽  
The Neural Networks ◽  
Processing Block

Abstract As a fundamental component of biological neurons, dendrites have been proven to have crucial effects in neuronal activities. Single neurons with dendrite structures show high signal processing capability that is analogous to a multilayer perceptron, whereas oversimplified point neuron models are still prevalent in AI algorithms and neuromorphic systems and fundamentally limit their efficiency and functionality of the systems constructed. In this study, we propose a dual-mode dendritic device based on electrolyte gated transistor, which can be operated to generate both supralinear and sublinear current-voltage responses when receiving input voltage pulses. We propose and demonstrate that the dual-mode dendritic devices can be used as a dendritic processing block between weight matrices and output neurons so as to enhance the expression ability of the neural networks. A dual-mode dendrites-enhanced neural network is therefore constructed with only two trainable parameters in the second layer, thus achieving 1000× reduction in the amount of second layer parameter compared to multilayer perceptron. After training by back propagation, the network reaches 90.1% accuracy in MNIST handwritten digits classification, showing advantage of the present dual-mode dendritic devices in building highly efficient neuromorphic computing.

Recognizing Odor Mixtures Using Optimized Fuzzy Neural Network Through Genetic Algorithms

2005 ◽  
Vol 9 (3) ◽  
pp. 290-296 ◽  
Author(s):  
Benyamin Kusumoputro ◽  
◽  
Teguh P. Arsyad
Keyword(s):  
Neural Network ◽  
Genetic Algorithms ◽  
Multilayer Perceptron ◽  
Probabilistic Neural Network ◽  
Back Propagation ◽  
Recognition System ◽  
Back Propagation Neural Network ◽  
Odor Recognition ◽  
Fuzzy Neural ◽  
Odor Mixtures

Recognizing odor mixtures is rather difficult in artificial odor recognition system, especially when the number of sensors is limited. Classification is further hampered if the number of unlearned odor mixtures classes is increased. We developed a fuzzy-neuro multilayer perceptron as a pattern classifier and compared its recognition with that of the Probabilistic Neural Network and Back-propagation Neural Network. To enhance the recognition capability of the system, we then optimized fuzzy-neuro multilayer perceptron topology by deleting its weak weight connections using Genetic Algorithms. Experimental results show that the optimized fuzzy-neuro multilayer perceptron has the highest recognition in 18 classes of two-mixture odors with almost 98.2% when using hardware with 16 sensors, compared to 83.3% when using 8 sensors.

Classification of EEG signals for epileptic seizures using Levenberg-Marquardt algorithm based Multilayer Perceptron Neural Network

2018 ◽  
Vol 34 (3) ◽  
pp. 1669-1677 ◽  
Author(s):  
Ankit Narang ◽  
Bhumika Batra ◽  
Arpit Ahuja ◽  
Jyoti Yadav ◽  
Nikhil Pachauri
Keyword(s):  
Neural Network ◽  
Multilayer Perceptron ◽  
Epileptic Seizures ◽  
Eeg Signals ◽  
Marquardt Algorithm ◽  
Levenberg Marquardt

scholarly journals NEURAL NETWORKS FOR THE PREDICTION OF FRESH PROPERTIES AND COMPRESSIVE STRENGTH OF FLOWABLE CONCRETE

2019 ◽  
pp. 183-197
Author(s):  
Revathy Jayaseelan ◽  
Gajalskshmi Pandulu ◽  
Ashwini G
Keyword(s):  
Neural Network ◽  
Compressive Strength ◽  
Network Model ◽  
Neural Network Model ◽  
Conjugate Gradient ◽  
Back Propagation ◽  
Fresh Properties ◽  
Levenberg Marquardt ◽  
Oxide Particles ◽  
Quasi Newton

This paper presents the prediction of fresh concrete properties and compressive strength of flowable concrete through neural network approach. A comprehensive data set was generated from the experiments performed in the laboratory under standard conditions. The flowable concrete was made with two different types of micro particles and with single nano particles. The input parameter was chosen for the neural network model as cement, fine aggregate, coarse aggregate, superplasticizer, water-cement ratio, micro aluminium oxide particles, micro titanium oxide particles, and nano silica. The output parameter includes the slump Flow, L-Box flow, V Funnel flow and compressive strength of the flowable concrete. To develop a suitable neural network model, several training algorithms were used such as BFGS Quasi- Newton back propagation, Fletcher-Powell conjugate gradient back propagation, Polak - Ribiere conjugate gradient back propagation, Gradient descent with adaptive linear back propagation and Levenberg-Marquardt back propagation. It was found that BFGS Quasi- Newton back propagation and Levenberg-Marquardt back propagation algorithm provides more than 90% on the prediction accuracy. Hence, the model performance was agreeable for prediction purposes for the fresh properties and compressive strength of flowable concrete.

scholarly journals Optimization of LMBP high-speed railway wheel size prediction algorithm based on improved adaptive differential evolution algorithm

2019 ◽  
Vol 15 (10) ◽  
pp. 155014771988134 ◽  
Author(s):  
Yu Zhang ◽  
Jiawen Zhang ◽  
Lin Luo ◽  
Xiaorong Gao
Keyword(s):  
Neural Network ◽  
Differential Evolution ◽  
Back Propagation ◽  
Differential Evolution Algorithm ◽  
Back Propagation Neural Network ◽  
Set Size ◽  
Adaptive Differential Evolution ◽  
Levenberg Marquardt ◽  
Evolution Algorithm ◽  
Local Extreme

It is beneficial for maintenance department to make maintenance strategy and reduce maintenance cost to forecast the hidden danger index value. Based on the analysis of the research status of wheel-to-life prediction at home and abroad and the repair of wheel-set wear and tear, this article designs and implements an adaptive differential evolution algorithm Levenberg–Marquardt back propagation wheel-set size prediction model. Aiming at the shortcomings of back propagation neural network, it is easy to fall into local extreme value. The back propagation algorithm is improved by Levenberg–Marquardt numerical optimization algorithm. Aiming at the shortcomings of back propagation neural network algorithm for randomly initializing connection weights and thresholds to fall into local extreme value, the differential evolution algorithm is used to optimize the initial connection weights and thresholds between the layers of the neural network. In order to speed up the search of the optimal initial weights and thresholds of the differential evolution algorithm Levenberg–Marquardt back propagation neural network, the initial values are further optimized, and an adaptive differential evolution algorithm Levenberg–Marquardt back propagation wheel-set size prediction model is designed and implemented. Compared with the proposed combine adaptive differential evolution algorithm with LMBP optimization (ADE-LMBP) is effective and significantly improves the prediction accuracy.

Sign in / Sign up

Export Citation Format

Share Document