Development of input variable selection and structural optimization algorithm for recurrent neural network

Data classification has been considered extensively in different fields, such as machine learning, artificial intelligence, pattern recognition, and data mining, and the expansion of classification has yielded immense achievements. The automatic classification of maintenance data has been investigated over the past few decades owing to its usefulness in construction and facility management. To utilize automated data classification in the maintenance field, a data classification model is implemented in this study based on the analysis of different mechanical maintenance data. The developed model involves four main steps: (a) data acquisition, (b) feature extraction, (c) feature selection, and (d) classification. During data acquisition, four types of dataset are collected from the benchmark Google datasets. The attributes of each dataset are further processed for classification. Principal component analysis and first-order and second-order statistical features are computed during the feature extraction process. To reduce the dimensions of the features for error-free classification, feature selection was performed. The hybridization of two algorithms, the Whale Optimization Algorithm (WOA) and Spotted Hyena Optimization (SHO), tends to produce a new algorithm—i.e., a Spotted Hyena-based Whale Optimization Algorithm (SH-WOA), which is adopted for performing feature selection. The selected features are subjected to a deep learning algorithm called Recurrent Neural Network (RNN). To enhance the efficiency of conventional RNNs, the number of hidden neurons in an RNN is optimized using the developed SH-WOA. Finally, the efficacy of the proposed model is verified utilizing the entire dataset. Experimental results show that the developed model can effectively solve uncertain data classification, which minimizes the execution time and enhances efficiency.

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The Realisation of Neural Network Structural Optimization Algorithm

Proceedings of the 2017 Federated Conference on Computer Science and Information Systems ◽

10.15439/2017f448 ◽

2017 ◽

Author(s):

Grzegorz Nowakowski ◽

Yaroslaw Dorogyy ◽

Olena Doroga-Ivaniuk

Keyword(s):

Neural Network ◽

Structural Optimization ◽

Optimization Algorithm

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Optimized recurrent neural network with fuzzy classifier for data prediction using hybrid optimization algorithm: Scope towards diverse applications

International Journal of Wavelets Multiresolution and Information Processing ◽

10.1142/s0219691320500745 ◽

2020 ◽

pp. 2050074

Author(s):

A. S. Prakaash ◽

K. Sivakumar

Keyword(s):

Neural Network ◽

Feature Extraction ◽

Prediction Model ◽

Recurrent Neural Network ◽

Optimization Algorithm ◽

Fuzzy Regression ◽

Hybrid Optimization ◽

Hybrid Optimization Algorithm ◽

Data Prediction ◽

Fuzzy Regression Model

Today, data processing has become a challenging task due to the significant increase in the amount of data collected using various sensors. To put up knowledge and forecast the data, the existing data mining techniques compute all numerical attributes in the memory simultaneously. However, the over-abundance of entire factors in the data makes accurate prediction infeasible. This paper attempts to implement a new data prediction model using an optimized machine learning algorithm. The proposed data prediction model involves four main phases: (a) data acquisition, (b) feature extraction, (c) data normalization, and (d) prediction. Initially, few data from the UCI repository like Bike Sharing Dataset, Carbon Nanotubes, Concrete Compressive Strength, Electrical Grid Stability Simulated Data, and SkillCraft-1 Master Table are collected. Further, the feature extraction process extracts the first-order statistics like mean, median, standard deviation, the maximum value of entire data, and the minimum value of entire data, and the second-order statistics like kurtosis, skewness, energy, and entropy. Next, the data or feature normalization is done to arrange the data within a certain limit. The normalized features are then subjected to a hybrid prediction system by integrating the Recurrent Neural Network (RNN) and Fuzzy Regression model. As a modification, the number of hidden neurons in the RNN and membership limits of the Fuzzy Regression model are optimized by a hybrid optimization algorithm by merging the concepts of Whale Optimization Algorithm (WOA) and Cat Swarm Optimization (CSO), which is called the Whale Updated Seek Mode-based CSO (WS-CSO) algorithm. Then, the efficiency of the optimized hybrid classifier for all-time prediction of data in different applications is confirmed based on its valuable performance and comparative analysis.

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Horse Optimization Algorithm Based Recurrent Neural Network Method for Epileptic Seizures Classification

IFMBE Proceedings - 7th International Conference on Advancements of Medicine and Health Care through Technology ◽

10.1007/978-3-030-93564-1_20 ◽

2022 ◽

pp. 183-190

Author(s):

Dorin Moldovan

Keyword(s):

Neural Network ◽

Recurrent Neural Network ◽

Optimization Algorithm ◽

Epileptic Seizures ◽

Neural Network Method ◽

Network Method

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

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331220938205 ◽

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.

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