Non-Gaussian Hybrid Transfer Functions: Memorizing Mine Survivability Calculations

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.

Simple and Efficient Computational Intelligence Strategies for Effective Collaborative Decisions

“…Notwithstanding the fact that, some recent advances [18,19] used DNNs for filtering tasks and shown encouraging consequences, auxiliary information of users were modelled, for instance, in audio and images. From literature [20], the massive data generated in recent times is as a result of generations from multi-modal, multi-dimensional datasets from current Recommender Systems (Rss) Hybrid algorithms [21] as proposed in this paper and being represented are used to optimize real-world implementations of algorithms and have received significant interest in recent years and are increasingly used to solve real-world problems as propounded by [22]. These hybrid models could include combination of two or more algorithms such as particle swarm optimization (PSO) [23], matrix factorization [24], genetic algorithms (GA) [25] and other computational strategies like artificial intelligence or deep neural networks [26] including but not limited to fuzzy logic systems [27], simulation, sigmoid functions or MLP [28], radial basis functions [29], just to mention a few.…”

“…These hybrid models could include combination of two or more algorithms such as particle swarm optimization (PSO) [23], matrix factorization [24], genetic algorithms (GA) [25] and other computational strategies like artificial intelligence or deep neural networks [26] including but not limited to fuzzy logic systems [27], simulation, sigmoid functions or MLP [28], radial basis functions [29], just to mention a few. Deep neural networks (DNNs) are techniques of artificial intelligence (AI) that have the capability to learn from experiences, it is robust [30] and improves performance by adapting to the changes in the environment [22]. The underlying advantages of Deep Neural Networks are the possibility of efficient operation of large amounts of data and its ability to generalize the outcome [19,31] proposed neural collaborating filtering which showed significant improvements but used matrix factorization approach which as far as we are concerned has additional computational burden.…”

“…represent the user ingrain of MTF and the MLP side as in the case of[22]; ReLU is used as the activation function of the MLP stratum. This model effectively joints the effectiveness of MTF and efficiency of DNNs harnessing the propensity of multimodal datasets of users of a recommender system hence; "NeuralFil," is short for Neural Tensor Filtering.…”