Abstract
Hybrid algorithms and models have received significant interest in recent years and are increasingly used to solve real-world problems. Different from existing methods in radial basis transfer function construction, this study proposes a novel nonlinear-weight hybrid algorithm involving the non-Gaussian type radial basis transfer functions. The speed and simplicity of the non-Gaussian type with the accuracy and simplicity of radial basis function are used to produce fast and accurate on-the-fly model for survivability of emergency mine rescue operations, that is, the survivability under all conditions is precalculated and used to train the neural network. The proposed hybrid uses genetic algorithm as a learning method which performs parameter optimization within an integrated analytic framework, to improve network efficiency. Finally, the network parameters including mean iteration, standard variation, standard deviation, convergent time, and optimized error are evaluated using the mean squared error. The results demonstrate that the hybrid model is able to reduce the computation complexity, increase the robustness and optimize its parameters. This novel hybrid model shows outstanding performance and is competitive over other existing models.
Highlights
Hybrid algorithms are used in optimizing real-world implementations that is, it comes as the best optimization solution tends to have challenges in implementation cost, time, and so forth, that needs a solution by using another technique
Different from existing methods in radial basis function (RBF) neural network construction and multilayer perceptron, this paper proposes a novel hybrid (HSRF) feed-forward algorithm involving the multilayer perceptron and non-Gaussian type radial basis transfer functions which is robust and performs parameter optimization within an integrated analytic framework, leading to two main technical advantages
Further justification for a research like this is that the simple imitations of the human brain demonstrate fast and accurate learning and classification properties in problems that otherwise require human experts. Such tools cannot obviously replace human experts, they are used as on-the-fly diagnostic tools and supporting evidences in quick decision making. With these in mind the main objective of this study is to investigate and improve upon the Gaussian radial basis function and develop a non-Gaussian hybrid of multilayer perceptrons (MLP) and the compact radial basis functions (CRBF) with enhanced optimization features
Summary
Hybrid algorithms are used in optimizing real-world implementations that is, it comes as the best optimization solution tends to have challenges in implementation cost, time, and so forth, that needs a solution by using another technique. Hybrid algorithms have received significant interest in recent years and are increasingly use to solve real-world problems These hybrid algorithms or models include combination of two or more algorithms involving genetic algorithms (GA) [1], particle swarm optimization (PSO) [2], and other computational techniques such as, artificial intelligence or neural networks including but not limited to multilayer perceptrons (MLP) or sigmoid [3], radial basis functions (RBF) [4, 5], fuzzy systems [6] and simulation annealing [7]. The underlying advantage(s) of ANNs are the possibility of efficient operation of large amounts of data and its ability to generalize the outcome This training algorithm, the ANNs, is largely used in applications involving classification or function approximation, and it has been proved that several classes of ANN are universal function approximations [9]. Taking into consideration the great potential of these techniques, this paper aims to establish a hybrid model using multilayer perceptron network (MLP)
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