scholarly journals Neural Network Approach to Modelling Transport System Resilience for Major Cities: Case Studies of Lagos and Kano (Nigeria)

2021 ◽  
Vol 13 (3) ◽  
pp. 1371
Author(s):  
Suleiman Hassan Otuoze ◽  
Dexter V. L. Hunt ◽  
Ian Jefferson
Keyword(s):  
Neural Network ◽  
Transport System ◽  
Mean Squared Error ◽  
Back Propagation ◽  
Modern Method ◽  
Urban Traffic ◽  
Propagation Model ◽  
Urban Life ◽  
Design Standards ◽  
Neural Network Approach

Congestion has become part of everyday urban life, and resilience is very crucial to traffic vulnerability and sustainable urban mobility. This research employed a neural network as an adaptive artificially-intelligent application to study the complex domains of traffic vulnerability and the resilience of the transport system in Nigerian cities (Kano and Lagos). The input criteria to train and check the models for the neural resilience network are the demographic variables, the geospatial data, traffic parameters, and infrastructure inventories. The training targets were set as congestion elements (traffic volume, saturation degree and congestion indices), which are in line with the relevant design standards obtained from the literature. A multi-layer feed-forward and back-propagation model involving input–output and curve fitting (nftool) in the MATLAB R2019b software wizard was used. Three algorithms—including Levenberg–Marquardt (LM), Bayesian Regularization (BR), and a Scaled Conjugate Gradient (SCG)—were selected for the simulation. LM converged easily with the Mean Squared Error (MSE) (2.675 × 10−3) and regression coefficient (R) (1.0) for the city of Lagos. Furthermore, the LM algorithm provided a better fit for the model training and for the overall validation of the Kano network analysis with MSE (4.424 × 10−1) and R (1.0). The model offers a modern method for the simulation of urban traffic and discrete congestion prediction.

scholarly journals Assess Reliability Parameters of an Electronic Voting Machine using a Neural Network Technique

2020 ◽  
Vol 9 (6) ◽  
pp. 269-275
Keyword(s):  
Neural Network ◽  
Storage System ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Descent Method ◽  
Electronic Voting ◽  
Gradient Descent Method ◽  
Neural Network Approach ◽  
Reliability Parameters ◽  
Voting Machine

The structure of Electronic Voting Machine (EVM) is an interconnected network of discrete components that record and count the votes of voters. The EVM system consists of four main subsystems which are Mother board of computer, Voting keys, Database storage system, power supply (AC and DC) along with various conditions of functioning as well as deficiency. The deficiency or failure of system is due to its components (hardware), software and human mismanagement. It is essential to reduce complexity of interconnected components and increase system reliability. Reliability analysis helps to identify technical situations that may affect the system and to predict the life of the system in future. The aim of this research paper is to analyze the reliability parameters of an EVM system using one of the approaches of computational intelligence, the neural network (NN). The probabilistic equations of system states and other reliability parameters are established for the proposed EVM model using neural network approach. It is useful for predicting various reliability parameters and improves the accuracy and consistency of parameters. To guarantee the reliability of the system, Back Propagation Neural Network (BPNN) architecture is used to learn a mechanism that can update the weights which produce optimal parameters values. Numerical examples are considered to authenticate the results of reliability, unreliability and profit function. To minimize the error and optimize the output in the form of reliability using gradient descent method, authors iterate repeatedly till the precision of 0.0001 error using MATLAB code. These parameters are of immense help in real time applications of Electronic Voting Machine during elections.

Design of Intelligent Network to Predicate Phenol Removal from Waste Water by Emulsion Liquid Membrane

2021 ◽  
Vol 1021 ◽  
pp. 115-128
Author(s):  
Suheila Abd Alreda Akkar ◽  
Sawsan Abd Muslim Mohammed
Keyword(s):  
Neural Network ◽  
Liquid Membrane ◽  
Mean Squared Error ◽  
Back Propagation ◽  
Volume Ratio ◽  
Phenol Removal ◽  
Acid Extraction ◽  
Training Algorithms ◽  
Membrane Phase ◽  
Emulsion Liquid Membrane

This research introduced Intelligent Network's proposed design for predicting efficiency in the removal of phenol from wastewater by liquid membrane emulsion. In the inner phase of W / O emulsions, phenol extraction from an aqueous solution was investigated using emulsion liquid membrane prepared with kerosene as a membrane phase, Span 80 as a surfactant, and NaOH as a stripping agent. Experiments were conducted to investigate the effect of three emulsion composition variables, namely: surfactant concentration, membrane phase to-internal (VM / VI) volume ratio, and removal phase concentration in the internal phase, and two process parameters, feed phase agitation speed at organic acid extraction rates, and emulsion-to-feed volume ratio (VE / VF). More than 98% of phenol can be extracted in less than 5 minutes. This article describes compares the performance of different learning algorithms such as GD, RB, GDM, GDX, CG, and LM to predict the efficiency of phenol removal from wastewater through the liquid emulsion membrane. The proposed neural network consisted of (7, 11, 1) neurons in the input , hidden and output layers respectively feed forward ANN with various types of back propagation training algorithms were developed to model the emulsion liquid membrane removal of phenols. The values predicted for the neural network model are found in close agreement with the results of the batch experiment using MATLAB program with a correlation coefficient ( R2) of 0.999 and Mean Squared Error (MSE) of 0.004.

scholarly journals Identification of Crack Length and Angle at the Center Weld Seam of Offshore Platforms Using a Neural Network Approach

2020 ◽  
Vol 8 (1) ◽  
pp. 40 ◽  
Author(s):  
Qingxi Yang ◽  
Gongbo Li ◽  
Weilei Mu ◽  
Guijie Liu ◽  
Hailiang Sun
Keyword(s):  
Neural Network ◽  
Crack Length ◽  
Structural Damage ◽  
Evaluation Method ◽  
Weld Seam ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Offshore Platforms ◽  
Neural Network Approach ◽  
External Interference

The reconstruction algorithm for the probabilistic inspection of damage (RAPID) is aimed at localizing structural damage via the signal difference coefficient (SDC) between the signals of the present and reference conditions. However, tomography is only capable of presenting the approximate location and not the length and angle of defects. Therefore, a new quantitative evaluation method called the multiple back propagation neural network (Multi-BPNN) is proposed in this work. The Multi-BPNN employs SDC values as input variables and outputs the predicted length and angle, with each output node depending on an individual hidden layer. The cracks of different lengths and angles at the center weld seam of offshore platforms are simulated numerically. The SDC values of the simulations and experiments were normalized for each sample to eliminate external interference in the experiments. Then, the normalized simulation data were employed to train the proposed neural network. The results of the simulations and experimental verification indicated that the Multi-BPNN can effectively predict crack length and angle, and has better stability and generalization capacity than the multi-input to multi-output back propagation neural network.

THE STAG OIL FIELD FORMATION EVALUATION: A NEURAL NETWORK APPROACH

1999 ◽  
Vol 39 (1) ◽  
pp. 451 ◽  
Author(s):  
H. Crocker ◽  
C.C. Fung ◽  
K.W. Wong
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Oil Field ◽  
Training Data ◽  
Ann Model ◽  
Neural Network Approach ◽  
Data Set ◽  
Formation Evaluation ◽  
Core Data

The producing M. australis Sandstone of the Stag Oil Field is a bioturbated glauconitic sandstone that is difficult to evaluate using conventional methods. Well log and core data are available for the Stag Field and for the nearby Centaur–1 well. Eight wells have log data; six also have core data.In the past few years artificial intelligence has been applied to formation evaluation. In particular, artificial neural networks (ANN) used to match log and core data have been studied. The ANN approach has been used to analyse the producing Stag Field sands. In this paper, new ways of applying the ANN are reported. Results from simple ANN approach are unsatisfactory. An integrated ANN approach comprising the unsupervised Self-Organising Map (SOM) and the Supervised Back Propagation Neural Network (BPNN) appears to give a more reasonable analysis.In this case study the mineralogical and petrophysical characteristics of a cored well are predicted from the 'training' data set of the other cored wells in the field. The prediction from the ANN model is then used for comparison with the known core data. In this manner, the accuracy of the prediction is determined and a prediction qualifier computed.This new approach to formation evaluation should provide a match between log and core data that may be used to predict the characteristics of a similar uncored interval. Although the results for the Stag Field are satisfactory, further study applying the method to other fields is required.

scholarly journals Development of combined back propagation algorithm and radial basis function for diagnosing depression patients

2015 ◽  
Vol 4 (1) ◽  
pp. 244
Author(s):  
Bhuvana R. ◽  
Purushothaman S. ◽  
Rajeswari R. ◽  
Balaji R.G.
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Mean Squared Error ◽  
Back Propagation ◽  
Optimum Number ◽  
Back Propagation Algorithm ◽  
Efficient System ◽  
Propagation Algorithm ◽  
Radial Basis

Depression is a severe and well-known public health challenge. Depression is one of the most common psychological problems affecting nearly everyone either personally or through a family member. This paper proposes neural network algorithm for faster learning of depression data and classifying the depression. Implementation of neural networks methods for depression data mining using Back Propagation Algorithm (BPA) and Radial Basis Function (RBF) are presented. Experimental data were collected with 21 depression variables used as inputs for artificial neural network (ANN) and one desired category of depression as the output variable for training and testing proposed BPA/RBF algorithms. Using the data collected, the training patterns, and test patterns are obtained. The input patterns are pre-processed and presented to the input layer of BPA/RBF. The optimum number of nodes required in the hidden layer of BPA/RBF is obtained, based on the change in the mean squared error dynamically, during the successive sets of iterations. The output of BPA is given as input to RBF. Through the combined topology, the work proves to be an efficient system for diagnosis of depression.

scholarly journals Interactive Genetic Algorithm Oriented toward the Novel Design of Traditional Patterns

Information ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 36 ◽  
Author(s):  
Jian Lv ◽  
Miaomiao Zhu ◽  
Weijie Pan ◽  
Xiang Liu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Bp Neural Network ◽  
Surrogate Model ◽  
Mean Squared Error ◽  
Back Propagation ◽  
Interactive Evolutionary Computation ◽  
Interactive Genetic Algorithm ◽  
Novel Design ◽  
Fuzzy Interval

To create alternative complex patterns, a novel design method is introduced in this study based on the error back propagation (BP) neural network user cognitive surrogate model of an interactive genetic algorithm with individual fuzzy interval fitness (IGA-BPFIF). First, the quantitative rules of aesthetic evaluation and the user’s hesitation are used to construct the Gaussian blur tool to form the individual’s fuzzy interval fitness. Then, the user’s cognitive surrogate model based on the BP neural network is constructed, and a new fitness estimation strategy is presented. By measuring the mean squared error, the surrogate model is well managed during the evolution of the population. According to the users’ demands and preferences, the features are extracted for the interactive evolutionary computation. The experiments show that IGA-BPFIF can effectively design innovative patterns matching users’ preferences and can contribute to the heritage of traditional national patterns.

scholarly journals The method of array antenna constructive synthesis on the basis of neural network approach

2019 ◽  
Vol 30 ◽  
pp. 05001
Author(s):  
Sergey Mishchenko ◽  
Vitaliy Shatskiy ◽  
Alexey Litvinov ◽  
Denis Eliseev
Keyword(s):  
Neural Network ◽  
Active Element ◽  
Back Propagation ◽  
Array Antenna ◽  
Antenna Element ◽  
Network Approach ◽  
Passive Element ◽  
Neural Network Approach ◽  
Neural Network Learning ◽  
Error Back Propagation

The method to decision constructive synthesis of array antennas was conducted. The method usefull when antenna elements can be in discreste states (for example: active element, passive element, excluded item, active element with discrete nominal of output power e.t.c). The method is based on neural network approach. The structure of a neural network consist of a classifying neural network and several approximating neural networks is substantiated. Input signals correspond to phase centers of array antenna elements. Number of output signals in classifying part is equal to discrete status of antenna element. Each approximating part of network has one output signal wich correspond to continious meaning. Separate parts of network preliminary learning with error back propagation method. The genetic algorithm of neural network learning with limited number of training coefficients is proposed. Examples of solving problems of constructive synthesis, with different indicators of the quality of neural network training are given.

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