An efficient grasshopper optimization with recurrent neural network controller-based induction motor to replace flywheel of the process machine

2020 ◽  
pp. 014233122093820
Author(s):  
Vasant M Jape ◽  
Hiralal M Suryawanshi ◽  
Jayant P Modak
Keyword(s):  
Neural Network ◽  
Objective Function ◽  
Induction Motor ◽  
Recurrent Neural Network ◽  
Optimization Algorithm ◽  
Power Electronic ◽  
Control Approach ◽  
Electronic Circuitry ◽  
Grasshopper Optimization Algorithm ◽  
Grasshopper Optimization

This paper proposes a convenient power electronic circuitry with a control approach for the flywheel replacement of an induction motor. The proposed control approach is the joined execution of grasshopper optimization algorithm and recurrent neural network based on duty ratio controller and hence the proposed work is named grasshopper optimization with recurrent neural network. The main contribution of this work is, the power electronic circuitry gets the input voltage samples and limits the deviation to appraise the instantaneous torque demand. The required voltage for the instantaneous torque demand is produced by the proposed control technique. In the proposed grasshopper optimization with recurrent neural network technique, the grasshopper optimization algorithm is a meta-heuristic population-based algorithm, which works from the perspective of the swarming behavior of grasshoppers in nature. In the proposed system, the recurrent neural network learning procedure is improved by the grasshopper optimization algorithm in the perspective of the minimum error objective function. the proposed grasshopper optimization with recurrent neural network technique optimizes the inverter switching states by limiting the error between the setpoint torque and the demand torque regarding objective function. With this proposed technique, the unbalance between demand torque and generated torque is found with high precision and the quicker execution to pull back out the torsional pulsation insensitive load linked transmission systems. By utilizing the proposed methodology, the extreme fluctuation of load torque due to peaky loads in an induction motor will be detected accurately. Also, the proposed technique reduces the torsional vibrations, weakness in components and minimizes the outages of uninterrupted production leading to higher profits. The proposed strategy is actualized in the MATLAB/Simulink platform and evaluated their performance. The performances are appeared differently compared with the existing methods.

scholarly journals The improved grasshopper optimization algorithm and its applications

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Peng Qin ◽  
Hongping Hu ◽  
Zhengmin Yang
Keyword(s):  
Neural Network ◽  
Optimization Algorithm ◽  
Bp Neural Network ◽  
Optimization Problems ◽  
Stock Exchange ◽  
Gravity Force ◽  
Shanxi Province ◽  
Grasshopper Optimization Algorithm ◽  
Predicted Model ◽  
Grasshopper Optimization

AbstractGrasshopper optimization algorithm (GOA) proposed in 2017 mimics the behavior of grasshopper swarms in nature for solving optimization problems. In the basic GOA, the influence of the gravity force on the updated position of every grasshopper is not considered, which possibly causes GOA to have the slower convergence speed. Based on this, the improved GOA (IGOA) is obtained by the two updated ways of the position of every grasshopper in this paper. One is that the gravity force is introduced into the updated position of every grasshopper in the basic GOA. And the other is that the velocity is introduced into the updated position of every grasshopper and the new position are obtained from the sum of the current position and the velocity. Then every grasshopper adopts its suitable way of the updated position on the basis of the probability. Finally, IGOA is firstly performed on the 23 classical benchmark functions and then is combined with BP neural network to establish the predicted model IGOA-BPNN by optimizing the parameters of BP neural network for predicting the closing prices of the Shanghai Stock Exchange Index and the air quality index (AQI) of Taiyuan, Shanxi Province. The experimental results show that IGOA is superior to the compared algorithms in term of the average values and the predicted model IGOA-BPNN has the minimal predicted errors. Therefore, the proposed IGOA is an effective and efficient algorithm for optimization.

Improved Linear Factor based Grasshopper Optimization Algorithm with Ensemble Learning for Covid-19 Forecasting

2021 ◽  
Vol 19 (8) ◽  
pp. 169-181
Author(s):  
P. Renukadevi ◽  
Dr.A. Rajiv Kannan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Ensemble Learning ◽  
Optimization Algorithm ◽  
Technological Development ◽  
Predictive Performance ◽  
Linear Factor ◽  
Corona Virus ◽  
Grasshopper Optimization Algorithm ◽  
Grasshopper Optimization

Recently the COVID’19 is extensively increasing around the world with many challenges for researchers. Rigorous respiratory disease corona virus 2 show aggression to many parts of COVID’19 affected patients, together with brain and lungs. The changeableness of Corona virus with likely to infect Central Nervous System emphasize the necessity for technological development to identify, handle, and take care of brain damages in COVID’19 patients. An exact short-term predicting the quantity of newly infected and cured cases is vital for resource optimization to stop or reduce the growth of infection. The previous system designed a Linear Decreasing Inertia Weight based Cat Swarm Optimization with Half Binomial Distribution based Convolutional Neural Network (LDIWCSO-HBDCNN) approach for COVID-19 forecasting. However, the ensemble learning is required to improve the prediction outcome via integrating many approaches. This approach allows the production of better predictive performance compared to a single model. For solving this problem, the proposed system designed an Improved Linear Factor based Grasshopper Optimization Algorithm with Ensemble Learning (ILFGOA with EL) for covid-19 forecasting. Initially, the COVID-19 forecasting dataset is taken as an input. With the help of min-max approach, data normalization is done. Then the optimal features are selected by using Improved Linear Factor based Grasshopper Optimization Algorithm (ILFGOA) algorithm to improve the prediction accuracy. Based on the selected features, Ensemble Learning (EL) which includes Hyperparameter based Convolutional Neural Network (HCNN) is utilized to identify infected and demise cases across india for a period of time. The outcome of analysis shows that the introduced method attains better execution against previous system with regard to error rate, accuracy, precision, recall and f-measure.

Classification of clothing images based on a parallel convolutional neural network and random vector functional link optimized by the grasshopper optimization algorithm

2021 ◽  
pp. 004051752110592
Author(s):  
Zhiyu Zhou ◽  
Wenxiong Deng ◽  
Yaming Wang ◽  
Zefei Zhu
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Convolutional Neural Network ◽  
Random Vector ◽  
Optimization Algorithm ◽  
Activation Function ◽  
Support Vector ◽  
Functional Link ◽  
Grasshopper Optimization Algorithm ◽  
Grasshopper Optimization

To improve accuracy in clothing image recognition, this paper proposes a clothing classification method based on a parallel convolutional neural network (PCNN) combined with an optimized random vector functional link (RVFL). The method uses the PCNN model to extract features of clothing images. Then, the structure-intensive and dual-channel convolutional neural network (i.e., the PCNN) is used to solve the problems of traditional convolutional neural networks (e.g., limited data and prone to overfitting). Each convolutional layer is followed by a batch normalization layer, and the leaky rectified linear unit activation function and max-pooling layers are used to improve the performance of the feature extraction. Then, dropout layers and fully connected layers are used to reduce the amount of calculation. The last layer uses the RVFL as optimized by the grasshopper optimization algorithm to replace the SoftMax layer and classify the features, further improving the stability and accuracy of classification. In this study, two aspects of the classification (feature extraction and feature classification) are improved, effectively improving the accuracy. The experimental results show that on the Fashion-Mnist dataset, the accuracy of the algorithm in this study reaches 92.93%. This value is 1.36%, 2.05%, 0.65%, and 3.76% higher than that of the local binary pattern (LBP)-support vector machine (SVM), histogram of oriented gradients (HOG)-SVM, LBP-HOG-SVM, and AlexNet-sparse representation-based classifier algorithms, respectively, effectively demonstrating the classification performance of the algorithm.

scholarly journals Speed controller design for three-phase induction motor based on dynamic adjustment grasshopper optimization algorithm

2021 ◽  
Vol 11 (2) ◽  
pp. 1143
Author(s):  
Ammar Falah Algamluoli ◽  
Nizar Hadi Abbas
Keyword(s):  
Induction Motor ◽  
Optimization Algorithm ◽  
Applied Load ◽  
Pi Controller ◽  
Dynamic Adjustment ◽  
Load Torque ◽  
Speed Controller ◽  
Three Phase ◽  
Grasshopper Optimization Algorithm ◽  
Grasshopper Optimization

Three-phase induction motor (TIM) is widely used in industrial application like paper mills, water treatment and sewage plants in the urban area. In these applications, the speed of TIM is very important that should be not varying with applied load torque. In this study, direct on line (DOL) motor starting without controller is modelled to evaluate the motor response when connected directly to main supply. Conventional PI controller for stator direct current and stator quadrature current of induction motor are designed as an inner loop controller as well as a second conventional PI controller is designed in the outer loop for controlling the TIM speed. Proposed combined PI-lead (CPIL) controllers for inner and outer loops are designed to improve the overall performance of the TIM as compared with the conventional controller. In this paper, dynamic adjustment grasshopper optimization algorithm (DAGOA) is proposed for tuning the proposed controller of the system. Numerical results based on well-selected test function demonstrate that DAGOA has a better performance in terms of speed of convergence, solution accuracy and reliability than SGOA. The study results revealed that the currents and speed of TIM system using CPIL-DAGOA are faster than system using conventional PI and CPIL controllers tuned by SGOA. Moreover, the speed controller of TIM system with CPIL controlling scheme based on DAGOA reached the steady state faster than others when applied load torque.

scholarly journals A New Hybrid Grasshopper Optimization - Backpropagation for Feedforward Neural Network Training

2020 ◽  
Vol 25 (1) ◽  
pp. 118
Author(s):  
Samer Alsammarraie ◽  
Nazar K. Hussein
Keyword(s):  
Neural Network ◽  
Optimization Algorithm ◽  
Hybrid Algorithm ◽  
Back Propagation ◽  
Global Optimum ◽  
Bp Algorithm ◽  
Back Propagation Algorithm ◽  
Grasshopper Optimization Algorithm ◽  
Grasshopper Optimization ◽  
Searching Ability

The Grasshopper optimization algorithm showed a rapid converge in the initial phases of the global search, however while being around the global optimum, the searching process became so slow. On the contrary, the gradient descending method around achieved faster convergent speed global optimum, and the convergent accuracy was showed to be higher at the same time. As a result, the proposed hybrid algorithm combined Grasshopper optimization algorithm (GOA) along with the back-propagation (BP) algorithm, also referred to as GOA–BP algorithm, was introduced to provide training to the weights of the feed forward neural network (FNN), the proposed hybrid algorithm can utilize the strong global searching ability of the GOA, and the intense local searching ability of the Back-Propagation algorithm. The results of experiments showed that the proposed hybrid GOA–BP algorithm was better and faster in convergent speed and accuracy than the Grasshopper optimization algorithm (GOA) and BP algorithm.   http://dx.doi.org/10.25130/tjps.25.2020.018  

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