Forward Kinematic Modelling with Radial Basis Function Neural Network Tuned with a Novel Meta-Heuristic Algorithm for Robotic Manipulators

The complexity of forward kinematic modelling increases with the increase in the degrees of freedom for a manipulator. To reduce the computational weight and time lag for desired output transformation, this paper proposes a forward kinematic model mapped with the help of the Radial Basis Function Neural Network (RBFNN) architecture tuned by a novel meta-heuristic algorithm, namely, the Cooperative Search Optimisation Algorithm (CSOA). The architecture presented is able to automatically learn the kinematic properties of the manipulator. Learning is accomplished iteratively based only on the observation of the input–output relationship. Related simulations are carried out on a 3-Degrees of Freedom (DOF) manipulator on the Robot Operating System (ROS). The dataset created from the simulation is divided 65–35 for training–testing of the proposed model. The metrics used for model validation include spread value, cost and runtime for the training dataset, and Mean Relative Error, Normal Mean Square Error, and Mean Absolute Error for the testing dataset. A comparative analysis of the CSOA-RBFNN model is performed with an artificial neural network, support vector regression model, and with with other meta-heuristic RBFNN models, i.e., PSORBFNN and GWO-RBFNN, that show the effectiveness and superiority of the proposed technique.publishedVersio

Highly efficient maximum power point tracking control technique for PV system under dynamic operating conditions

publishedVersio

Adaptive ML-based technique for renewable energy system power forecasting in hybrid PV-Wind farms power conversion systems

Large scale integration of renewable energy system with classical electrical power generation system requires a precise balance to maintain and optimize the supply–demand limitations in power grids operations. For this purpose, accurate forecasting is needed from wind energy conversion systems (WECS) and solar power plants (SPPs). This daunting task has limits with long-short term and precise term forecasting due to the highly random nature of environmental conditions. This paper offers a hybrid variational decomposition model (HVDM) as a revolutionary composite deep learning-based evolutionary technique for accurate power production forecasting in microgrid farms. The objective is to obtain precise short-term forecasting in five steps of development. An improvised dynamic group-based cooperative search (IDGC) mechanism with a IDGC-Radial Basis Function Neural Network (IDGC-RBFNN) is proposed for enhanced accurate short-term power forecasting. For this purpose, meteorological data with time series is utilized. SCADA data provide the values to the system. The improvisation has been made to the metaheuristic algorithm and an enhanced training mechanism is designed for the short term wind forecasting (STWF) problem. The results are compared with two different Neural Network topologies and three heuristic algorithms: particle swarm intelligence (PSO), IDGC, and dynamic group cooperation optimization (DGCO). The 24 h ahead are studied in the experimental simulations. The analysis is made using seasonal behavior for year-round performance analysis. The prediction accuracy achieved by the proposed hybrid model shows greater results. The comparison is made statistically with existing works and literature showing highly effective accuracy at a lower computational burden. Three seasonal results are compared graphically and statistically.publishedVersio

A Novel Artificial Neural Network (ANN) Using The Mayfly Algorithm for Classification

acceptedVersio

A comprehensive framework for hand gesture recognition using hybrid-metaheuristic algorithms and deep learning models

This paper presents a novel methodology that utilizes gesture recognition data, which are collected with a Leap Motion Controller (LMC), in tandem with the Spotted Hyena-based Chimp Optimization Algorithm (SSC) for feature selection and training of deep neural networks (DNNs). An expansive tabular database was created using the LMC for eight distinct gestures and the SSC algorithm was used for discerning and selecting salient features. This refined feature subset is then utilized in the subsequent training of a DNN model. A comprehensive comparative analysis is conducted to evaluate the performance of the SSC algorithm in comparison with established optimization techniques, such as Particle Swarm Optimization(PSO), Grey Wolf Optimizer(GWO), and Sine Cosine Algorithm(SCA), specifically in the context of feature selection. The empirical findings decisively establish the efficacy of the SSC algorithm, consistently achieving a high accuracy rate of 98% in the domain of gesture recognition tasks. The feature selection approach proposed emphasizes its intrinsic capacity to enhance not only the accuracy of gesture recognition systems and its wider suitability across diverse domains that require sophisticated feature extraction techniques

Improved Reptile Search Optimization Algorithm: Application on Regression and Classification Problems

The reptile search algorithm is a newly developed optimization technique that can efficiently solve various optimization problems. However, while solving high-dimensional nonconvex optimization problems, the reptile search algorithm retains some drawbacks, such as slow convergence speed, high computational complexity, and local minima trapping. Therefore, an improved reptile search algorithm (IRSA) based on a sine cosine algorithm and Levy flight is proposed in this work. The modified sine cosine algorithm with enhanced global search capabilities avoids local minima trapping by conducting a full-scale search of the solution space, and the Levy flight operator with a jump size control factor increases the exploitation capabilities of the search agents. The enhanced algorithm was applied to a set of 23 well-known test functions. Additionally, statistical analysis was performed by considering 30 runs for various performance measures like best, worse, average values, and standard deviation. The statistical results showed that the improved reptile search algorithm gives a fast convergence speed, low time complexity, and efficient global search. For further verification, improved reptile search algorithm results were compared with the RSA and various state-of-the-art metaheuristic techniques. In the second phase of the paper, we used the IRSA to train hyperparameters such as weight and biases for a multi-layer perceptron neural network and a smoothing parameter (σ) for a radial basis function neural network. To validate the effectiveness of training, the improved reptile search algorithm trained multi-layer perceptron neural network classifier was tested on various challenging, real-world classification problems. Furthermore, as a second application, the IRSA-trained RBFNN regression model was used for day-ahead wind and solar power forecasting. Experimental results clearly demonstrated the superior classification and prediction capabilities of the proposed hybrid model. Qualitative, quantitative, comparative, statistical, and complexity analysis revealed improved global exploration, high efficiency, high convergence speed, high prediction accuracy, and low time complexity in the proposed technique

Adaptive ML-based technique for renewable energy system power forecasting in hybrid PV-Wind farms power conversion systems

Large scale integration of renewable energy system with classical electrical power generation system requires a precise balance to maintain and optimize the supply–demand limitations in power grids operations. For this purpose, accurate forecasting is needed from wind energy conversion systems (WECS) and solar power plants (SPPs). This daunting task has limits with long-short term and precise term forecasting due to the highly random nature of environmental conditions. This paper offers a hybrid variational decomposition model (HVDM) as a revolutionary composite deep learning-based evolutionary technique for accurate power production forecasting in microgrid farms. The objective is to obtain precise short-term forecasting in five steps of development. An improvised dynamic group-based cooperative search (IDGC) mechanism with a IDGC-Radial Basis Function Neural Network (IDGC-RBFNN) is proposed for enhanced accurate short-term power forecasting. For this purpose, meteorological data with time series is utilized. SCADA data provide the values to the system. The improvisation has been made to the metaheuristic algorithm and an enhanced training mechanism is designed for the short term wind forecasting (STWF) problem. The results are compared with two different Neural Network topologies and three heuristic algorithms: particle swarm intelligence (PSO), IDGC, and dynamic group cooperation optimization (DGCO). The 24 h ahead are studied in the experimental simulations. The analysis is made using seasonal behavior for year-round performance analysis. The prediction accuracy achieved by the proposed hybrid model shows greater results. The comparison is made statistically with existing works and literature showing highly effective accuracy at a lower computational burden. Three seasonal results are compared graphically and statistically

Training Deep Neural Networks with Novel Metaheuristic Algorithms for Fatigue Crack Growth Prediction in Aluminum Aircraft Alloys

Fatigue cracks are a major defect in metal alloys, and specifically, their study poses defect evaluation challenges in aluminum aircraft alloys. Existing inline inspection tools exhibit measurement uncertainties. The physical-based methods for crack growth prediction utilize stress analysis models and the crack growth model governed by Paris’ law. These models, when utilized for long-term crack growth prediction, yield sub-optimum solutions and pose several technical limitations to the prediction problems. The metaheuristic optimization algorithms in this study have been conducted in accordance with neural networks to accurately forecast the crack growth rates in aluminum alloys. Through experimental data, the performance of the hybrid metaheuristic optimization–neural networks has been tested. A dynamic Levy flight function has been incorporated with a chimp optimization algorithm to accurately train the deep neural network. The performance of the proposed predictive model has been tested using 7055 T7511 and 6013 T651 alloys against four competing techniques. Results show the proposed predictive model achieves lower correlation error, least relative error, mean absolute error, and root mean square error values while shortening the run time by 11.28%. It is evident through experimental study and statistical analysis that the crack length and growth rates are predicted with high fidelity and very high resolution

Step towards secure and reliable smart grids in Industry 5.0: A federated learning assisted hybrid deep learning model for electricity theft detection using smart meters

The integration of Smart Grid technology and conceptual Industry 5.0 has paved the way for advanced energy management systems that enhance efficiency and revolutionized the parallel integration of power sources in a sustainable manner. However, this digitization has opened a new stream of the threat and opportunities of electricity theft posing a significant challenge to the security and reliability of Smart Grid networks. In this paper, we propose a secure and reliable theft detection technique using deep federated learning (FL) mechanism. The technique leverages the collaborative power of FL to train a Convolutional Gated Recurrent Unit (ConvGRU) model on distributed data sources without compromising data privacy. The training deep learning model backbone consists of a ConvGRU model that combines convolutional and gated recurrent units to capture spatial and temporal patterns in electricity consumption data. An improvised preprocessing mechanism and hyperparameter tuning is done to facilitate FL mechanism. The halving randomized search algorithm is used for hyperparameters tuning of the ConvGRU model. The impact of hyperparameters involved in the ConvGRU model such as number of layers, filters, kernel size, activation function, pooling, GRU layers, hidden state dimension, learning rate, and the dropout rate is elaborated. The proposed technique achieves promising results, with high accuracy, precision, recall, and F1 score, demonstrating its efficacy in detecting electricity theft in Smart Grid networks. Comparative analysis with existing techniques reveal the superior performance of the deep FL-based ConvGRU model. The findings highlight the potential of this approach in enhancing the security and efficiency of Smart Grid systems while preserving data privacy