Solar Radiation Estimation Based on a New Combined Approach of Artificial Neural Networks (ANN) and Genetic Algorithms (GA) in South Algeria

When designing solar systems and assessing the effectiveness of their many uses,estimating sun irradiance is a crucial first step.This study examined three approaches(ANN,GA-ANN,and ANFIS)for estimating daily global solar radiation(GSR)in the south of Algeria:Adrar,Ouargla,and Bechar.The proposed hybrid GA-ANN model,based on genetic algorithm-based optimization,was developed to improve the ANN model.The GA-ANN and ANFIS models performed better than the standalone ANN-based model,with GA-ANN being better suited for forecasting in all sites,and it performed the best with the best values in the testing phase of Coefficient of Determination(R=0.9005),Mean Absolute Percentage Error(MAPE=8.40%),and Relative Root Mean Square Error(rRMSE=12.56%).Nevertheless,the ANFIS model outperformed the GA-ANN model in forecasting daily GSR,with the best values of indicators when testing the model being R=0.9374,MAPE=7.78%,and rRMSE=10.54%.Generally,we may conclude that the initial ANN stand-alone model performance when forecasting solar radiation has been improved,and the results obtained after injecting the genetic algorithm into the ANN to optimize its weights were satisfactory.The model can be used to forecast daily GSR in dry climates and other climates and may also be helpful in selecting solar energy system installations and sizes.

Combining the genetic algorithms with artificial neural networks for optimization of board allocating

This paper introduced the Genetic Algorithms (GAs) and Artificial Neural Networks (ANNs), which have been widely used in optimization of allocating. The combination way of the two optimizing algorithms was used in board allocating of furniture production. In the experiment, the rectangular flake board of 3650 mm 1850 mm was used as raw material to allocate 100 sets of Table Bucked. The utilizing rate of the board reached 94.14 % and the calculating time was only 35 s. The experiment result proofed that the method by using the GA for optimizing the weights of the ANN can raise the utilizing rate of the board and can shorten the time of the design. At the same time, this method can simultaneously searched in many directions, thus greatly in-creasing the probability of finding a global optimum.

A Fractional-Order Ultra-Local Model-Based Adaptive Neural Network Sliding Mode Control of n-DOF Upper-Limb Exoskeleton With Input Deadzone

This paper proposes an adaptive neural network sliding mode control based on fractional-order ultra-local model for n-DOF upper-limb exoskeleton in presence of uncertainties,external disturbances and input deadzone.Considering the model complexity and input deadzone,a fractional-order ultra-local model is proposed to formulate the original dynamic system for simple controller design.Firstly,the control gain of ultra-local model is considered as a constant.The fractional-order sliding mode technique is designed to stabilize the closed-loop system,while fractional-order time-delay estimation is combined with neural network to estimate the lumped disturbance.Correspondingly,a fractional-order ultra-local model-based neural network sliding mode controller(FO-NNSMC) is proposed.Secondly,to avoid disadvantageous effect of improper gain selection on the control performance,the control gain of ultra-local model is considered as an unknown parameter.Then,the Nussbaum technique is introduced into the FO-NNSMC to deal with the stability problem with unknown gain.Correspondingly,a fractional-order ultra-local model-based adaptive neural network sliding mode controller(FO-ANNSMC) is proposed.Moreover,the stability analysis of the closed-loop system with the proposed method is presented by using the Lyapunov theory.Finally,with the co-simulations on virtual prototype of 7-DOF iReHave upper-limb exoskeleton and experiments on 2-DOF upper-limb exoskeleton,the obtained compared results illustrate the effectiveness and superiority of the proposed method.

Automatic Identification of Tomato Maturation Using Multilayer Feed Forward Neural Network with Genetic Algorithms (GA)

We set up computer vision system for tomato images. By using this system, the RGB value of tomato image was converted into HIS value whose H was used to acquire the color character of the surface of tomato. To use multilayer feed forward neural network with GA can finish automatic identification of tomato maturation. The results of experiment showed that the accuracy was up to 94%.

Optimizing neural networks by genetic algorithms for predicting particulate matter concentration in summer in Beijing

We developed and tested an improved neural network to predict the average concentration of PM10(particulate matter with diameter smaller than 10 ?m) several hours in advance in summer in Beijing.A genetic algorithm optimization procedure for optimizing initial weights and thresholds of the neural network was also evaluated.This research was based upon the PM10 data from seven monitoring sites in Beijing urban region and meteorological observation data,which were recorded every 3 h during summer of 2002.Two neural network models were developed.Model I was built for predicting PM10 concentrations 3 h in advance while Model II for one day in advance.The predictions of both models were found to be consistent with observations.Percent errors in forecasting the numerical value were about 20.This brings us to the conclusion that short-term fluctuations of PM10 concentrations in Beijing urban region in summer are to a large extent driven by meteorological conditions.Moreover,the predicted results of Model II were compared with the ones provided by the Models-3 Community Multiscale Air Quality(CMAQ) modeling system.The mean relative errors of both models were 0.21 and 0.26,respectively.The performance of the neural network model was similar to numerical models,when applied to short-time prediction of PM10 concentration.

Research on Power Control of Wind Power Generation Based on Neural Network Adaptive Control

For the characteristics of wind power generation system is multivariable, nonlinear and random, in this paper the neural network PID adaptive control is adopted. The size of pitch angle is adjusted in time to improve the perfomance of power control. The PID parameters are corrected by the gradient descent method, and Radial Basis Functiion （RBF） neural network is used as the system identifier in this method. Sinlation results show that by using neural network adaptive PID controller the generator power control can inhibit effectively the speed and affect the output prover of generator. The dynamic performnce and robustness of the controlled system is good, and the peformance of wind power system is improved.

Rapid Application of Neural Networks and A Genetic Algorithms to Solidified Aging Processes for Copper Alloy

Rapidly solidified aging is an effective way to refine the microstructure of Cu-Cr-Sn-Zn lead frame alloy and enhance its hardness. The artificial neural network methodology(ANN) along with genetic algorithms were used for data analysis and optimization. In this paper the input parameters of the artificial neural network (ANN) are the aging temperature and aging time. The outputs of the ANN model are the hardness and conductivity properties. Some explanations of these predicted results from the microstructure and precipitation-hardening viewpoint are given. After the ANN model is trained successfully, genetic algorithms(GAs) are applied for optimizing the aging processes parameters.

NEURAL NETWORK-BASED ADAPTIVE CONTROL OF A CLASS OFUNCERTAIN NONLINEAR SYSTEMS

For a general class of nonlinear systems with uncertainty, an adaptive control scheme based on combining sliding control ideas with neural network is proposed, the control law is to control the plant to track a desired reference signal within a prespecified precision. A global convergence result is provided.

Neural network fault diagnosis method optimization with rough set and genetic algorithms

Aiming at the disadvantages of BP model in artificial neural networks applied to intelligent fault diagnosis, neural network fault diagnosis optimization method with rough sets and genetic algorithms are presented. The neural network nodes of the input layer can be calculated and simplified through rough sets theory; The neural network nodes of the middle layer are designed through genetic algorithms training; the neural network bottom-up weights and bias are obtained finally through the combination of genetic algorithms and BP algorithms. The analysis in this paper illustrates that the optimization method can improve the performance of the neural network fault diagnosis method greatly.

Underwater vehicle sonar self-noise prediction based on genetic algorithms and neural network

The factors that influence underwater vehicle sonar self-noise are analyzed, and genetic algorithms and a back propagation (BP) neural network are combined to predict underwater vehicle sonar self-noise. The experimental results demonstrate that underwater vehicle sonar self-noise can be predicted accurately by a GA-BP neural network that is based on actual underwater vehicle sonar data.

Application of Genetic Algorithms to Optimize Neural Networks for Selected Tribological Tests

This paper presents a method of determining the friction coefficient in metal forming using multilayer artificial neural networks based on experimental data obtained from strip drawing test. The number of input variables of the artificial neural network has been optimized using genetic algorithm. This process is based on surface parameters of the sheet and dies, sheet material parameters and clamping force as input parameters to train the neural network. In addition to demonstrating the fact that regression statistics model using genetic selection and intelligent problem solver are better than models without preprocessing of input data, the sensitivity analysis of the input variables has been conducted. This avoids the time-consuming testing of neurons in finding the best network architecture. The obtained results from this study have also pointed out that genetic algorithm can successfully be applied to optimize the training set and the outputs agree with experimental results. This allows reduction or elimination of expensive experimental tests to determine friction coefficient value.

Optimization of Processing Parameters of Power Spinning for Bushing Based on Neural Network and Genetic Algorithms

A neural network model of key process parameters and forming quality is developed based on training samples which are obtained from the orthogonal experiment and the finite element numerical simulation. Optimization of the process parameters is conducted using the genetic algorithm (GA). The experimental results have shown that a surface model of the neural network can describe the nonlinear implicit relationship between the parameters of the power spinning process:the wall margin and amount of expansion. It has been found that the process of determining spinning technological parameters can be accelerated using the optimization method developed based on the BP neural network and the genetic algorithm used for the process parameters of power spinning formation. It is undoubtedly beneficial towards engineering applications.

DETERMINING THE STRUCTURES AND PARAMETERS OF RADIAL BASIS FUNCTION NEURAL NETWORKS USING IMPROVED GENETIC ALGORITHMS

The method of determining the structures and parameters of radial basis function neural networks(RBFNNs) using improved genetic algorithms is proposed. Akaike′s information criterion (AIC) with generalization error term is used as the best criterion of optimizing the structures and parameters of networks. It is shown from the simulation results that the method not only improves the approximation and generalization capability of RBFNNs ,but also obtain the optimal or suboptimal structures of networks.

A Spectral Convolutional Neural Network Model Based on Adaptive Fick’s Law for Hyperspectral Image Classification

Hyperspectral image classification stands as a pivotal task within the field of remote sensing,yet achieving highprecision classification remains a significant challenge.In response to this challenge,a Spectral Convolutional Neural Network model based on Adaptive Fick’s Law Algorithm(AFLA-SCNN)is proposed.The Adaptive Fick’s Law Algorithm(AFLA)constitutes a novel metaheuristic algorithm introduced herein,encompassing three new strategies:Adaptive weight factor,Gaussian mutation,and probability update policy.With adaptive weight factor,the algorithmcan adjust theweights according to the change in the number of iterations to improve the performance of the algorithm.Gaussianmutation helps the algorithm avoid falling into local optimal solutions and improves the searchability of the algorithm.The probability update strategy helps to improve the exploitability and adaptability of the algorithm.Within the AFLA-SCNN model,AFLA is employed to optimize two hyperparameters in the SCNN model,namely,“numEpochs”and“miniBatchSize”,to attain their optimal values.AFLA’s performance is initially validated across 28 functions in 10D,30D,and 50D for CEC2013 and 29 functions in 10D,30D,and 50D for CEC2017.Experimental results indicate AFLA’s marked performance superiority over nine other prominent optimization algorithms.Subsequently,the AFLA-SCNN model was compared with the Spectral Convolutional Neural Network model based on Fick’s Law Algorithm(FLA-SCNN),Spectral Convolutional Neural Network model based on Harris Hawks Optimization(HHO-SCNN),Spectral Convolutional Neural Network model based onDifferential Evolution(DE-SCNN),SpectralConvolutionalNeuralNetwork(SCNN)model,and SupportVector Machines(SVM)model using the Indian Pines dataset and PaviaUniversity dataset.The experimental results show that the AFLA-SCNN model outperforms other models in terms of Accuracy,Precision,Recall,and F1-score on Indian Pines and Pavia University.Among them,the Accuracy of the AFLA-SCNN model on Indian Pines reached 99.875%,and the Accuracy on PaviaUniversity reached 98.022%.In conclusion,our proposed AFLA-SCNN model is deemed to significantly enhance the precision of hyperspectral image classification.

Lyapunov-Based Dynamic Neural Network for Adaptive Control of Complex Systems

In this paper, an adaptive neuro-control structure for complex dynamic system is proposed. A recurrent Neural Network is trained-off-line to learn the inverse dynamics of the system from the observation of the input-output data. The direct adaptive approach is performed after the training process is achieved. A lyapunov-Base training algorithm is proposed and used to adjust on-line the network weights so that the neural model output follows the desired one. The simulation results obtained verify the effectiveness of the proposed control method.

Identification and Adaptive Control of Dynamic Nonlinear Systems Using Sigmoid Diagonal Recurrent Neural Network

The goal of this paper is to introduce a new neural network architecture called Sigmoid Diagonal Recurrent Neural Network (SDRNN) to be used in the adaptive control of nonlinear dynamical systems. This is done by adding a sigmoid weight victor in the hidden layer neurons to adapt of the shape of the sigmoid function making their outputs not restricted to the sigmoid function output. Also, we introduce a dynamic back propagation learning algorithm to train the new proposed network parameters. The simulation results showed that the (SDRNN) is more efficient and accurate than the DRNN in both the identification and adaptive control of nonlinear dynamical systems.

Adaptive recurrent neural network for uncertainties estimation in feedback control system

In this paper,a recurrent neural network(RNN)is used to estimate uncertainties and implement feedback control for nonlinear dynamic systems.The neural network approximates the uncertainties related to unmodeled dynamics,parametric variations,and external disturbances.The RNN has a single hidden layer and uses the tracking error and the output as feedback to estimate the disturbance.The RNN weights are online adapted,and the adaptation laws are developed from the stability analysis of the controlled system with the RNN estimation.The used activation function,at the hidden layer,has an expression that simplifies the adaptation laws from the stability analysis.It is found that the adaptive RNN enhances the tracking performance of the feedback controller at the transient and steady state responses.The proposed RNN based feedback control is applied to a DC–DC converter for current regulation.Simulation and experimental results are provided to show its effectiveness.Compared to the feedforward neural network and the conventional feedback control,the RNN based feedback control provides good tracking performance.

NEURAL NETWORK PREDICTIVE CONTROL WITH HIERARCHICAL GENETIC ALGORITHM

A kind of predictive control based on the neural network(NN) for nonlinear systems with time delay is addressed.The off line NN model is obtained by using hierarchical genetic algorithms (HGA) to train a sequence data of input and output.Output predictions are obtained by recursively mapping the NN model.The error rectification term is introduced into a performance function that is directly optimized while on line control so that it overcomes influences of the mismatched model and disturbances,etc.Simulations show the system has good dynamic responses and robustness.

Time delay recursive neural network-based direct adaptive control for a piezo-actuated stage

Piezo-actuated stage is a core component in micro-nano manufacturing field.However,the inherent nonlinearity,such as rate-dependent hysteresis,in the piezo-actuated stage severely impacts its tracking accuracy.This study proposes a direct adaptive control(DAC)method to realize high precision tracking.The proposed controller is designed by a time delay recursive neural network.Compared with those existing DAC methods designed under the general Lipschitz condition,the proposed control method can be easily generalized to the actual systems,which have hysteresis behavior.Then,a hopfield neural network(HNN)estimator is proposed to adjust the parameters of the proposed controller online.Meanwhile,a modular model consisting of linear submodel,hysteresis submodel,and lumped uncertainties is established based on the HNN estimator to describe the piezoactuated stage in this study.Thus,the performance of the HNN estimator can be exhibited visually through the modeling results.The proposed control method eradicates the adverse effects on the control performance arising from the inaccuracy in establishing the offline model and improves the capability to suppress the influence of hysteresis on the tracking accuracy of piezo-actuated stage in comparison with the conventional DAC methods.The stability of the control system is studied.Finally,a series of comparison experiments with a dual neural networks-based data driven adaptive controller are carried out to demonstrate the superiority of the proposed controller.

A New Robust Adaptive Neural Network Backstepping Control for Single Machine Infinite Power System With TCSC

For a single machine infinite power system with thyristor controlled series compensation(TCSC) device, which is affected by system model uncertainties, nonlinear time-delays and external unknown disturbances, we present a robust adaptive backstepping control scheme based on the radial basis function neural network(RBFNN). The RBFNN is introduced to approximate the complex nonlinear function involving uncertainties and external unknown disturbances, and meanwhile a new robust term is constructed to further estimate the system residual error,which removes the requirement of knowing the upper bound of the disturbances and uncertainty terms. The stability analysis of the power system is presented based on the Lyapunov function,which can guarantee the uniform ultimate boundedness(UUB) of all parameters and states of the whole closed-loop system. A comparison is made between the RBFNN-based robust adaptive control and the general backstepping control in the simulation part to verify the effectiveness of the proposed control scheme.