A Review on Optimizing Radial Basis Function Neural Network using Nature Inspired Algorithm

Radial Basis Function (RBF) is a type of feed forward neural network .This function can be applied to interpolation, chaotic time-series modeling, control engineering, image restoration, data fusion etc. In RBF network, parameters of basis functions (such as width, the position and number of centers) in the nonlinear hidden layer have great influence on the performance of the network. Common RBF training algorithms cannot possibly find the global optima of nonlinear parameters in the hidden layer, and often have too many hidden units to reach certain approximation abilities, which will lead to too large a scale for the network and decline of generalization ability. Also, 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. Secondly, the Swarm Intelligence Algorithms are (Meta-Heuristic) development Algorithms, which attracted much attention and appeared its ability in the last ten years within many applications such as data mining, scheduling, improve the performance of artificial neural networks (ANN) and classification. So, in this paper the work of Artificial Bee Colony (ABC), Genetic algorithm(GA), Particle swarm optimization(PSO) and Bat algorithm(BA) have been reviewed, which optimized the RBF neural network in their own terms

A Review on the Application of Natural Computing in Environmental Informatics

Natural computing offers new opportunities to understand, model and analyze the complexity of the physical and human-created environment. This paper examines the application of natural computing in environmental informatics, by investigating related work in this research field. Various nature-inspired techniques are presented, which have been employed to solve different relevant problems. Advantages and disadvantages of these techniques are discussed, together with analysis of how natural computing is generally used in environmental research.Comment: Proc. of EnviroInfo 201

Designing Artificial Neural Network Using Particle Swarm Optimization: A Survey

Neural network modeling has become a special interest for many engineers and scientists to be utilized in different types of data as time series, regression, and classification and have been used to solve complicated practical problems in different areas, such as medicine, engineering, manufacturing, military, business. To utilize a prediction model that is based upon artificial neural network (ANN), some challenges should be addressed that optimal designing and training of ANN are major ones. ANN can be defined as an optimization task because it has many hyper parameters and weights that can be optimized. Metaheuristic algorithms such as swarm intelligence-based methods are a category of optimization methods that aim to find an optimal structure of ANN and to train the network by optimizing the weights. One of the commonly used swarm intelligence-based algorithms is particle swarm optimization (PSO) that can be used for optimizing ANN. In this study, we review the conducted research works on optimizing the ANNs using PSO. All studies are reviewed from two different perspectives: optimization of weights and optimization of structure and hyper parameters

Artificial intelligence in the cyber domain: Offense and defense

Artificial intelligence techniques have grown rapidly in recent years, and their applications in practice can be seen in many fields, ranging from facial recognition to image analysis. In the cybersecurity domain, AI-based techniques can provide better cyber defense tools and help adversaries improve methods of attack. However, malicious actors are aware of the new prospects too and will probably attempt to use them for nefarious purposes. This survey paper aims at providing an overview of how artificial intelligence can be used in the context of cybersecurity in both offense and defense.Web of Science123art. no. 41