Intelligent computing in electrical utility industry 4.0 : concept, key technologies, applications and future directions

Industry 4.0 (I-4.0) is referred to as ‘fourth industrial revolution’ towards incorporation of artificial intelligence and digitalization of industrial systems. It is meticulously associated with the development and advancement of evolving technologies such as: Internet of Things, Cyber-Physical System, Information and Communications Technology, Enterprise Architecture, and Enterprise Integration. Power systems of today face several challenges that need to be addressed and application of these technologies can make the modern power systems become more effective, reliable, secure, and cost-effective. Therefore, a widespread analysis of I- 4.0 is performed in this paper and a summary of the outcomes, future scope, and real-world application of I- 4.0 on the electrical utility industry (EUI) is reported by reviewing the existing literature. This report will be helpful to the investigators interested in the area of I- 4.0 and for application in EUI.Analytical Center for Government of the Russian Federation.https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639Electrical, Electronic and Computer Engineerin

A novel nature inspired firefly algorithm with higher order neural network: Performance analysis

The applications of both Feed Forward Neural network and Multilayer perceptron are very diverse and saturated. But the linear threshold unit of feed forward networks causes fast learning with limited capabilities, while due to multilayering, the back propagation of errors exhibits slow training speed in MLP. So, a higher order network can be constructed by correlating between the input variables to perform nonlinear mapping using the single layer of input units for overcoming the above drawbacks. In this paper, a Firefly based higher order neural network has been proposed for data classification for maintaining fast learning and avoids the exponential increase of processing units. A vast literature survey has been conducted to review the state of the art of the previous developed models. The performance of the proposed method has been tested with various benchmark datasets from UCI machine learning repository and compared with the performance of other established models. Experimental results imply that the proposed method is fast, steady, reliable and provides better classification accuracy than others

A TLBO based gradient descent learning-functional link higher order ANN: An efficient model for learning from non-linear data

All the higher order ANNs (HONNs) including functional link ANN (FLANN) are sensitive to random initialization of weight and rely on the learning algorithms adopted. Although a selection of efficient learning algorithms for HONNs helps to improve the performance, on the other hand, initialization of weights with optimized weights rather than random weights also play important roles on its efficiency. In this paper, the problem solving approach of the teaching learning based optimization (TLBO) along with learning ability of the gradient descent learning (GDL) is used to obtain the optimal set of weight of FLANN learning model. TLBO does not require any specific parameters rather it requires only some of the common independent parameters like number of populations, number of iterations and stopping criteria, thereby eliminating the intricacy in selection of algorithmic parameters for adjusting the set of weights of FLANN model. The proposed TLBO-FLANN is implemented in MATLAB and compared with GA-FLANN, PSO-FLANN and HS-FLANN. The TLBO-FLANN is tested on various 5-fold cross validated benchmark data sets from UCI machine learning repository and analyzed under the null-hypothesis by using Friedman test, Holm’s procedure and post hoc ANOVA statistical analysis (Tukey test & Dunnett test)

A novel Chemical Reaction Optimization based Higher order Neural Network (CRO-HONN) for nonlinear classification

In this paper, a Chemical Reaction Optimization (CRO) based higher order neural network with a single hidden layer called Pi–Sigma Neural Network (PSNN) has been proposed for data classification which maintains fast learning capability and avoids the exponential increase of number of weights and processing units. CRO is a recent metaheuristic optimization algorithm inspired by chemical reactions, free from intricate operator and parameter settings such as other algorithms and loosely couples chemical reactions with optimization. The performance of the proposed CRO-PSNN has been tested with various benchmark datasets from UCI machine learning repository and compared with the resulting performance of PSNN, GA-PSNN, PSO-PSNN. The methods have been implemented in MATLAB and the accuracy measures have been tested by using the ANOVA statistical tool. Experimental results show that the proposed method is fast, steady and reliable and provides better classification accuracy than others

A Global-best Harmony Search based Gradient Descent Learning FLANN (GbHS-GDL-FLANN) for data classification

While dealing with real world data for classification using ANNs, it is often difficult to determine the optimal ANN classification model with fast convergence. Also, it is laborious to adjust the set of weights of ANNs by using appropriate learning algorithm to obtain better classification accuracy. In this paper, a variant of Harmony Search (HS), called Global-best Harmony Search along with Gradient Descent Learning is used with Functional Link Artificial Neural Network (FLANN) for classification task in data mining. The Global-best Harmony Search (GbHS) uses the concepts of Particle Swarm Optimization from Swarm Intelligence to improve the qualities of harmonies. The problem solving strategies of Global-best Harmony Search along with searching capabilities of Gradient Descent Search are used to obtain optimal set of weight for FLANN. The proposed method (GbHS-GDL-FLANN) is implemented in MATLAB and compared with other alternatives (FLANN, GA based FLANN, PSO based FLANN, HS based FLANN, Improved HS based FLANN, Self Adaptive HS based FLANN, MLP, SVM and FSN). The GbHS-GDL-FLANN is tested on benchmark datasets from UCI Machine Learning repository by using 5-fold cross validation technique. The proposed method is analyzed under null-hypothesis by using Friedman Test, Holm and Hochberg Procedure and Post-Hoc ANOVA Statistical Analysis (Tukey Test & Dunnett Test) for statistical analysis and validity of results. Simulation results reveal that the performance of the proposed GbHS-GDL-FLANN is better and statistically significant from other alternatives

A self adaptive harmony search based functional link higher order ANN for non-linear data classification

In the data classification process involving higher order ANNs, it’s a herculean task to determine the optimal ANN classification model due to non-linear nature of real world datasets. To add to the woe, it is tedious to adjust the set of weights of ANNs by using appropriate learning algorithm to obtain better classification accuracy. In this paper, an improved variant of harmony search (HS), called self-adaptive harmony search (SAHS) along with gradient descent learning is used with functional link artificial neural network (FLANN) for the task of classification in data mining. Using its past experiences, SAHS adjusts the harmonies according to the maximum and minimum values in the current harmony memory. The powerful combination of this unique strategy of SAHS and searching capabilities of gradient descent search is used to obtain optimal set of weights for FLANN. The proposed method (SAHS–FLANN) is implemented in MATLAB and the results are contrasted with other alternatives (FLANN, GA based FLANN, PSO based FLANN, HS based FLANN, improved HS based FLANN and TLBO based FLANN). To illustrate its effectiveness, SAHS–FLANN is tested on various benchmark datasets from UCI machine learning repository by using 5-fold cross validation technique. Under the null-hypothesis, the proposed method is analyzed by using various statistical tests for statistical correctness of results. The performance of the SAHS–FLANN is found to be better and statistically significant in comparison with other alternatives. The SAHS–FLANN differs from HS–FLANN (HS based FLANN) by the elimination of constant parameters (bandwidth and pitch adjustment rate). Furthermore, it leads to the simplification of steps for the improvisation of weight-sets in IHS–FLANN (improved HS based FLANN) by incorporating adjustments of new weight-sets according to the weight-sets with maximum and minimum fitness.Web of Science179876