scholarly journals Investigations of nonlinear induction motor model using the Gudermannian neural networks

2021 ◽  
pp. 261-261
Author(s):  
Zulqurnain Sabir ◽  
Muhammad Asif Raja ◽  
Dumitru Baleanu ◽  
R. Sadat ◽  
Mohamed Ali
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Local Search ◽  
Induction Motor ◽  
Fitness Function ◽  
Search Process ◽  
Mean Square ◽  
Active Set ◽  
Fifth Order ◽  
Motor Model

This study aims to solve the nonlinear fifth-order induction motor model (FO-IMM) using the Gudermannian neural networks (GNNs) along with the optimization procedures of global search as a genetic algorithm together with the quick local search process as active-set technique (GNN-GA-AST). GNNs are executed to discretize the nonlinear FO-IMM to prompt the fitness function in the procedure of mean square error. The exactness of the GNN-GA-AST is observed by comparing the obtained results with the reference results. The numerical performances of the stochastic GNN-GA-AST are provided to tackle three different variants based on the nonlinear FO-IMM to authenticate the consistency, significance and efficacy of the designed stochastic GNN-GA-AST. Additionally, statistical illustrations are available to authenticate the precision, accuracy and convergence of the designed stochastic GNN-GA-AST.

Genetic Algorithm (GA) and Internal Energy Transfer Calculations Using Neural Network (NN) Methods

2012 ◽  
Author(s):  
Lionel Raff ◽  
Ranga Komanduri ◽  
Martin Hagan ◽  
Satish Bukkapatnam
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Objective Function ◽  
Fitness Function ◽  
Solution Space ◽  
Large Ensemble ◽  
Nonlinear Functional ◽  
Squared Error ◽  
Time Required

Genetic algorithms (GA), like NNs, can be used to fit highly nonlinear functional forms, such as empirical interatomic potentials from a large ensemble of data. Briefly, a genetic algorithm uses a stochastic global search method that mimics the process of natural biological evolution. GAs operate on a population of potential solutions applying the principle of survival of the fittest to generate progressively better approximations to a solution. A new set of approximations is generated in each iteration (also known as generation) of a GA through the process of selecting individuals from the solution space according to their fitness levels, and breeding them together using operators borrowed from natural genetics. This process leads to the evolution of populations of individuals that have a higher probability of being “fitter,” i.e., better approximations of the specified potential values, than the individuals they were created from, just as in natural adaptation. The most time-consuming part in implementing a GA is often the evaluation of the objective or the fitness function. The objective function O[P] is expressed as sum squared error computed over a given large ensemble of data. Consequently, the time required for evaluating the objective function becomes an important factor. Since a GA is well suited for implementing on parallel computers, the time required for evaluating the objective function can be reduced significantly by parallel processing. A better approach would be to map out the objective function using several possible solutions concurrently or beforehand to improve computational efficiency of the GA prior to its execution, and using this information to implement the GA. This will obviate the need for cumbersome direct evaluation of the objective function. Neural networks may be best suited to map the functional relationship between the objective function and the various parameters of the specific functional form. This study presents an approach that combines the universal function approximation capability of multilayer neural networks to accelerate a GA for fitting atomic system potentials. The approach involves evaluating the objective function, which for the present application is the mean squared error (MSE) between the computed and model-estimated potential, and training a multilayer neural network with decision variables as input and the objective function as output.

Induction Motor Direct Torque Control Based on Multiple Neural Networks Optimized by Genetic Algorithm

2012 ◽  
Vol 482-484 ◽  
pp. 1985-1989
Author(s):  
Gan Zou ◽  
Tao Li ◽  
Ren Xin Xiao
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Simulation Model ◽  
Induction Motor ◽  
Direct Torque Control ◽  
Torque Control ◽  
Speed Sensor ◽  
Multiple Neural Networks ◽  
The Neural Networks ◽  
Simulation Results

Conventional direct torque control(DTC) of induction motor has the problem of large torque ripple.In addition,the speed sensor has its deficiency.A novel DTC system based on multiple neural networks optimized by Genetic Algorithm is proposed and the structures of the proposed system are designed.Genetic algorithm was used to optimize the initial weights and thresholds of the neural networks,All parameters of the neural networks were obtained by offline training.A simulation model of induction motor DTC system was developed in Matlab／Simulink,the simulation results show the feasibility and effectiveness of the scheme

scholarly journals Stochastic technique for solutions of non-linear fin equation arising in thermal equilibrium model

2020 ◽  
Vol 24 (5 Part A) ◽  
pp. 3013-3022
Author(s):  
Iftikhar Ahmad ◽  
Hina Qureshi ◽  
Muhammad Bilal ◽  
Muhammad Usman
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Interior Point ◽  
Hybrid Approach ◽  
Pattern Search ◽  
Active Set ◽  
Non Linear ◽  
Artificial Neural ◽  
Interior Point Technique

In this study, a stochastic numerical technique is used to investigate the numerical solution of heat transfer temperature distribution system using feed forward artificial neural networks. Mathematical model of fin equation is formulated with the help of artificial neural networks. The effect of the heat on a rectangular fin with thermal conductivity and temperature dependent internal heat generation is calculated through neural networks optimization with optimizers like active set technique, interior point technique, pattern search, genetic algorithm and a hybrid approach of pattern search - interior point technique, genetic algorithm - active set technique, genetic algorithm - interior point technique, and genetic algorithm - sequential quadratic programming with different selections of weights. The governing fin equation is transformed into an equivalent non-linear second order ODE. For this transformed ODE model we have performed several simulations to provide the justification of better convergence of results. Moreover, the effectiveness of the designed models is validated through a complete statistical analysis. This study reveals the importance of rectangular fins during the heat transformation through the system.

Evolutionary Learning Acquisition of Optimal Joint Angle Trajectories of Flexible Robot Arm

2006 ◽  
Vol 18 (1) ◽  
pp. 103-110 ◽  
Author(s):  
Hiroyuki Kojima ◽  
◽  
Takahiro Hiruma
Keyword(s):  
Genetic Algorithm ◽  
Joint Angle ◽  
Fitness Function ◽  
Vibration Reduction ◽  
Evolutionary Learning ◽  
Robot Arm ◽  
Flexible Robot ◽  
Residual Vibration ◽  
Fifth Order ◽  
Acquisition Method

This paper proposes the evolutionary learning acquisition method of the optimal joint angle trajectories of a flexible robot arm using the genetic algorithm is proposed, and the effects of the optimal joint angle trajectories obtained by the present evolutionary learning acquisition method on the residual vibration reduction are ascertained numerically and experimentally. In the construction of the evolutionary learning acquisition algorithm of the optimal joint angle trajectories, the joint angular velocity curves are depicted with fifth-order polynomials, and, by considering the boundary and constraint conditions, they are expressed by four parameters. Then, the residual vibrations of the flexible robot arm are expressed as a function of the chromosome consisting of four parameters, namely, four genes, and a fitness function of the genetic algorithm for the residual vibration reduction is defined. Furthermore, the numerical calculations have been carried out, and it is confirmed that the residual vibrations almost disappear. Moreover, the experimental results are demonstrated, and the usefulness of the present evolutionary learning acquisition method of the optimal joint angle trajectories of the flexible robot arm using the genetic algorithm is ascertained experimentally.

Selection of Predictor Variables for Pneumonia Using Neural Networks and Genetic Algorithms

2005 ◽  
Vol 44 (01) ◽  
pp. 89-97 ◽  
Author(s):  
B. S. Gerber ◽  
T. G. Tape ◽  
R. S. Wigton ◽  
P. S. Heckerling
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Logistic Regression ◽  
Fitness Function ◽  
Predictor Variable ◽  
Community Acquired Pneumonia ◽  
Stepwise Logistic Regression ◽  
Training Cohort ◽  
Input Layer ◽  
Roc Area

Summary Background: Artificial neural networks (ANN) can be used to select sets of predictor variable that incorporate nonlinear interactions between variables. We used a genetic algorithm, with selection based on maximizing network accuracy and minimizing network input-layer cardinality, to evolve parsimonious sets of variables for predicting community-acquired pneumonia among patients with respiratory complaints. Methods: ANN were trained on data from 1044 patients in a training cohort, and were applied to 116 patients in a testing cohort. Chromosomes with binary genes representing input-layer variables were operated on by crossover recombination, mutation, and probabilistic selection based on a fitness function incorporating both network accuracy and input-layer cardinality. Results: The genetic algorithm evolved best 10-variable sets that discriminated pneumonia in the training cohort (ROC areas, 0.838 for selection based on average cross entropy (ENT); 0.954 for selection based on ROC area (ROC)), and in the testing cohort (ROC areas, 0.847 for ENT selection; 0.963 for ROC selection), with no significant differences between cohorts. Best variable sets based on the genetic algorithm using ROC selection discriminated pneumonia more accurately than variable sets based on stepwise neural networks (ROC areas, 0.954 versus 0.879, p = 0.030), or stepwise logistic regression (ROC areas, 0.954 versus 0.830, p = 0.000). Variable sets of lower cardinalities were also evolved, which also accurately discriminated pneumonia. Conclusion: Variable sets derived using a genetic algorithm for neural networks accurately discriminated pneumonia from other respiratory conditions, and did so with greater accuracy than variables derived using stepwise neural networks or logistic regression in some cases.

TRAINING NEURAL NETWORKS BY MEANS OF GENETIC ALGORITHMS WORKING ON VERY LONG CHROMOSOMES

1995 ◽  
Vol 06 (03) ◽  
pp. 299-316 ◽  
Author(s):  
PETER G. KORNING
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Genetic Algorithms ◽  
Fitness Function ◽  
Multiple Representations ◽  
Real Life ◽  
Multilayer Perceptrons ◽  
Training Methods ◽  
Neural Net ◽  
The Neural Network

In the neural network/genetic algorithm community, rather limited success in the training of neural networks by genetic algorithms has been reported. In a paper by Whitley et al. (1991), he claims that, due to “the multiple representations problem”, genetic algorithms will not effectively be able to train multilayer perceptrons, whose chromosomal representation of its weights exceeds 300 bits. In the following paper, by use of a “real-life problem”, known to be non-trivial, and by a comparison with “classic” neural net training methods, I will try to show, that the modest success of applying genetic algorithms to the training of perceptrons, is caused not so much by the “multiple representations problem” as by the fact that problem-specific knowledge available is often ignored, thus making the problem unnecessarily tough for the genetic algorithm to solve. Special success is obtained by the use of a new fitness function, which takes into account the fact that the search performed by a genetic algorithm is holistic, and not local as is usually the case when perceptrons are trained by traditional methods.

scholarly journals Application of Genetic Algorithm Elements to Modelling of Rotation Processes in Motion Transmission Including a Long Shaft

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 115
Author(s):  
Andriy Chaban ◽  
Marek Lis ◽  
Andrzej Szafraniec ◽  
Radoslaw Jedynak
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Fitness Function ◽  
Least Square ◽  
Distributed Parameter ◽  
Parameter Method ◽  
Analytical Mechanics ◽  
Parameter System ◽  
Rotational Systems ◽  
Elastic Elements

Genetic algorithms are used to parameter identification of the model of oscillatory processes in complicated motion transmission of electric drives containing long elastic shafts as systems of distributed mechanical parameters. Shaft equations are generated on the basis of a modified Hamilton–Ostrogradski principle, which serves as the foundation to analyse the lumped parameter system and distributed parameter system. They serve to compute basic functions of analytical mechanics of velocity continuum and rotational angles of shaft elements. It is demonstrated that the application of the distributed parameter method to multi-mass rotational systems, that contain long elastic elements and complicated control systems, is not always possible. The genetic algorithm is applied to determine the coefficients of approximation the system of Rotational Transmission with Elastic Shaft by equivalent differential equations. The fitness function is determined as least-square error. The obtained results confirm that application of the genetic algorithms allow one to replace the use of a complicated distributed parameter model of mechanical system by a considerably simpler model, and to eliminate sophisticated calculation procedures and identification of boundary conditions for wave motion equations of long elastic elements.

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