Modeling of solar energy systems using artificial neural network: A comprehensive review

This book chapter proposes a new training algorithms for Radial Basis Function (RBF) using a recently proposed optimization algorithm called Moth–Flame Optimizer (MFO). After formulating MFO as RBFN trainer, seven standard binary classifications are employed as case studies. The MFO-based trainer is compared with Particle Swarm Algorithm (PSO), Genetic Algorithm (GA), Bat Algorithm (BA), and newrb. The results show that the proposed trainer is able to show superior results on the majority of case studies. The observation of convergence behavior proves that this new trainer benefits from accelerating convergence speed as well.

Evolving Radial Basis Function Networks Using Moth–Flame Optimizer

Background and aims: Since accurate forecasts help inform decisions for preventive health-careintervention and epidemic control, this goal can only be achieved by making use of appropriatetechniques and methodologies. As much as forecast precision is important, methods and modelselection procedures are critical to forecast precision. This study aimed at providing an overview ofthe selection of the right artificial neural network (ANN) methodology for the epidemic forecasts. It isnecessary for forecasters to apply the right tools for the epidemic forecasts with high precision.Methods: It involved sampling and survey of epidemic forecasts based on ANN. A comparison ofperformance using ANN forecast and other methods was reviewed. Hybrids of a neural network withother classical methods or meta-heuristics that improved performance of epidemic forecasts wereanalysed.Results: Implementing hybrid ANN using data transformation techniques based on improvedalgorithms, combining forecast models, and using technological platforms enhance the learning andgeneralization of ANN in forecasting epidemics.Conclusion: The selection of forecasting tool is critical to the precision of epidemic forecast; hence, aworking guide for the choice of appropriate tools will help reduce inconsistency and imprecision inforecasting epidemic size in populations. ANN hybrids that combined other algorithms and models,data transformation and technology should be used for an epidemic forecast.

/pdf/a-review-of-epidemic-forecasting-using-artificial-neural-1dpu66bnfk.pdf

A Review of Epidemic Forecasting Using Artificial Neural Networks

This paper presents an approach for predicting daily network traffic using artificial neural networks (ANN), namely radial basis function neural network (RBFNN) method. The data is gained from 21-24 June 2013 (192 samples series data) in ICT Unit of Mulawarman University, East Kalimantan, Indonesia. The results of measurement are using statistical analysis, e.g. sum of square error (SSE), mean of square error (MSE), mean of absolute percentage error (MAPE), and mean of absolute deviation (MAD). The results show that values are the same, with different goals that have been set are 0.001, 0.002, and 0.003, and spread 200. The smallest MSE value indicates a good method for accuracy. Therefore, the RBFNN model illustrates the proposed best model to predict daily network traffic.

/pdf/time-series-prediction-using-radial-basis-function-neural-28k5tq8km5.pdf

Time Series Prediction Using Radial Basis Function Neural Network

Radial Basis Function Neural Network (RBFNN) is very prominent in data processing. However, improving this technique is vital for the NN training process. This paper presents an integrated 2 Satisfiability in radial basis function neural network (RBFNN-2SAT). There are two different types of training in RBFNN, namely no-training technique and half-training technique. The performance of the solutions via Genetic Algorithm (GA) training was investigated by comparing the Radial Basis Function Neural Network No-Training Technique (RBFNN- 2SATNT) and Radial Basis Function Neural Network Half-Training Technique (RBFNN- 2SATHT). The comparison of both techniques was examined on 2 Satisfiability problem by using a C# software that was developed for this experiment. The performance of the RBFNN-2SATNT and RBFNN-2SATHT in performing 2SAT is discussed in terms of root mean squared error (RMSE), sum squared error (SSE), mean absolute percentage error (MAPE), mean absolute error (MAE), number of the hidden neurons and CPU time. Results obtained from a computer simulation showed that RBFNN-2SATHT outperformed RBFNN-2SATNT.

/pdf/radial-basis-function-neural-network-for-2-satisfiability-3nrg4no51e.pdf

Radial basis function neural network for 2 satisfiability programming

When confronting the complex problems, radial basis function (RBF) neural network has the advantages of adaptive and self-learning ability, but it is difficult to determine the number of hidden layer neurons, and the weights learning ability from hidden layer to the output layer is low; these deficiencies easily lead to decreasing learning ability and recognition precision. Aiming at this problem, we propose a new optimized RBF neural network algorithm based on genetic algorithm (GA-RBF algorithm), which uses genetic algorithm to optimize the weights and structure of RBF neural network; it chooses new ways of hybrid encoding and optimizing simultaneously. Using the binary encoding encodes the number of the hidden layer's neurons and using real encoding encodes the connection weights. Hidden layer neurons number and connection weights are optimized simultaneously in the new algorithm. However, the connection weights optimization is not complete; we need to use least mean square (LMS) algorithm for further leaning, and finally get a new algorithm model. Using two UCI standard data sets to test the new algorithm, the results show that the new algorithm improves the operating efficiency in dealing with complex problems and also improves the recognition precision, which proves that the new algorithm is valid.

/pdf/a-new-optimized-ga-rbf-neural-network-algorithm-28krx6cuqv.pdf

A new optimized GA-RBF neural network algorithm

High-dimensional large sample data sets, between feature variables and between samples, may cause some correlative or repetitive factors, occupy lots of storage space, and consume much computing time. Using the Elman neural network to deal with them, too many inputs will influence the operating efficiency and recognition accuracy; too many simultaneous training samples, as well as being not able to get precise neural network model, also restrict the recognition accuracy. Aiming at these series of problems, we introduce the partial least squares (PLS) and cluster analysis (CA) into Elman neural network algorithm, by the PLS for dimension reduction which can eliminate the correlative and repetitive factors of the features. Using CA eliminates the correlative and repetitive factors of the sample. If some subclass becomes small sample, with high-dimensional feature and fewer numbers, PLS shows a unique advantage. Each subclass is regarded as one training sample to train the different precise neural network models. Then simulation samples are discriminated and classified into different subclasses, using the corresponding neural network to recognize it. An optimized Elman neural network classification algorithm based on PLS and CA (PLS-CA-Elman algorithm) is established. The new algorithm aims at improving the operating efficiency and recognition accuracy. By the case analysis, the new algorithm has unique superiority, worthy of further promotion.

https://downloads.hindawi.com/journals/cin/2014/724317.pdf

Dean Zhao

Papers

A new optimized GA-RBF neural network algorithm

Study on optimized Elman neural network classification algorithm based on PLS and CA