Evaluation of Manufactured Product Performance Using Neural Networks

This paper discusses some of the several successful applications of neural networks which have made them a useful simulation tool. After several years of neglect, confidence in the accuracy of neural networks began to grow from the 1980s with applications in power, control and instrumentation and robotics to mention a few. Several successful industrial implementations of neural networks in the field of electrical engineering will be reviewed and results of the authors’ research in the areas of food security and health will also be presented. The research results will show that successful neural simulation results using Neurosolutions software also translated to successful realtime implementation of cost-effective products with reliable overall performance of up to 90%

Neural Estimation of Food Age with Adaline-based Multi-Layer Perceptron

This study employs a 4-input and 1-output feedforward neural network with adalines used to implement learning via error back-propagation (EBP) using least mean square rule. The neural network is used to predict the condition of both cooked and uncooked food as well as fresh vegetables by determining food age (in days). Neurosolutions training software is used to simulate the neural network. Training data is obtained from a constructed metal oxide semiconductor (MOS) ammonia circuit. Results show that a 95% overall accuracy of neural network results is obtained. This demonstrates the capability of neural networks in accurate classification of sample data points. Food samples used to obtain inference database include rice, beans, fresh vegetables, yam and potatoes.Keywords/Index Terms: neural network, supervised learning, back propagation, e-nose, artificial intelligenc

Neural Networks as a Tool for Product Manufacturing Innovation in Africa

-This paper highlights the numerous advantages of process simulation using neural networks. Apart from reviewing some successful industrial applications of neural networks (specifically in the field of electrical engineering), results of the authors' research in the areas of food security and health will also be presented. The research results will show that successful neural simulation results using Neurosolutions software also translated to successful realtime implementation of cost-effective products with reliable overall performance of up to 90%

Evaluation of Manufactured Product Performance Using Neural Networks

This paper discusses some of the several successful applications of neural networks which have made them a useful simulation tool. After several years of neglect, confidence in the accuracy of neural networks began to grow from the 1980s with applications in power, control and instrumentation and robotics to mention a few. Several successful industrial implementations of neural networks in the field of electrical engineering will be reviewed and results of the authors’ research in the areas of food security and health will also be presented. The research results will show that successful neural simulation results using Neurosolutions software also translated to successful realtime implementation of cost-effective products with reliable overall performance of up to 90%

Improved evolutionary algorithms with application to smart grid demand response management

D.Ing. (Electrical Engineering)Abstract: Evolutionary Algorithms (EAs) refer to a group of optimization strategies which are based on Darwin’s theory of natural selection. According to Darwin, attributes of an organism’s genotype are shared among its offspring when mating with another organism occurs. Through this continuous process of mating, combination (or recombination), mutation and selection, it becomes possible to obtain offspring with an optimal balance of gene attributes for both parent organisms. EAs include Evolutionary Strategies (ES), Evolutionary Programming (EP), Genetic Algorithms (GAs), and Differential Evolution (DE). This thesis presents a collection of articles which detail improvements to the performance of existing EAs (mainly based on GAs and DE). The motivation for this research arises from the fact that evolutionary algorithms suffer from inadequacies in handling multiple objectives and constraints which frequently occur in real world problems. In particular, Pareto-based GAs experience difficulty in balancing convergence and diversity in the presence of multiple time-varying objectives and constraints. DE encounters difficulty in finding the global optimum for multiple mutation vectors. This thesis proposes several niching-based strategies to improve the tradeoff between convergence and diversity for GAs. It also presents a 2-archive approach to improve crossover and recombination for DE with multiple mutation vectors. These improved algorithms are tested on both static and dynamic mathematical models representing selected aspects of smart grid, namely: distributed energy resource (DER) allocation, cost function minimization, and demand response management. Results show that the improved EAs provide better optimized parameters for the selected mathematical grid models compared to already existing algorithms. In particular, the improved EAs optimize reference point placement, utilize a pointer-based archiving approach, and adaptively vary crossover rate in order to achieve optimal convergence and diversity of the search population. With respect to GAs, the articles generally adopt a Pareto-based approach to search the solution space. Results obtained from applying a number of improved EA approaches demonstrate their effectiveness. The research in this thesis also details various directions for future research which are discussed at the end of the thesis.Evolutionary programming (Computer science

Neural Estimation of Food Age with Adaline-based Multi-Layer Perceptron

This study employs a 4-input and 1-output feedforward neural network with adalines used to implement learning via error back-propagation (EBP) using least mean square rule. The neural network is used to predict the condition of both cooked and uncooked food as well as fresh vegetables by determining food age (in days). Neurosolutions training software is used to simulate the neural network. Training data is obtained from a constructed metal oxide semiconductor (MOS) ammonia circuit. Results show that a 95% overall accuracy of neural network results is obtained. This demonstrates the capability of neural networks in accurate classification of sample data points. Food samples used to obtain inference database include rice, beans, fresh vegetables, yam and potatoes.Keywords/Index Terms: neural network, supervised learning, back propagation, e-nose, artificial intelligenc

Demand response modeling with solar PV as a panacea to the Nigerian electricity distribution conundrum: A case study of Sierra Leone

The role of solar PV in the global energy mix is becoming more significant as the cost of solar modules continues to decline. The African continent has a good degree of solar irradiation. Therefore, it is imperative that the benefits of alternative energy generation through solar PV are maximized. In this paper, we present an optimization model of a case study in Sierra Leone which can be adapted for the Nigerian distribution system. This is because both countries are located in the same region (West Africa) and power systems are similar. We consider two demand response (DR) scenarios: price-based and incentive-based. The various DR schemes are compared to a situation in which there is no DR utilization to examine the impact of DR on peak load reduction and financial benefit to participants, and impact of renewable energy dispatch during peak and off-peak periods on financial benefits to participants. Simulation results show that the time- and incentive-based DR strategies reduce peak load demand by 11% and 14% respectively