Hybrid neural network

About: Hybrid neural network is a research topic. Over the lifetime, 1305 publications have been published within this topic receiving 18223 citations.

A new model to forecast the results of matches based on hybrid neural networks in the soccer rating system

Abstract: The objective of this paper is to build a result prediction model for the rating system in soccer games. A rating system which plays a crucial role in world sports field yields predictions for the probability that one contestant beats another. The result prediction model is the core technique in the rating system. The robustness and accuracy of the model is a very important feature because people will trust the rating system only if it can give the exact prediction of the game results. This paper employs a coarse-to-fine training technique based on hybrid neural network. Very few people have ever attempted the method based on neural network before in this field. First a match is classified into three categories with a LVQ net to determine the strength contrast between two contestants. Then the elaborately designed data will go through the specific BP nets according to the classifying result. The model is trained and tested on volumes of actual soccer match results from Italian series A. Finally the results of the model are compared to other prediction models based on statistics. The outcome shows that the new model is more accurate and provides better performance evaluation of all teams.

Hybrid neural network–particle swarm optimization algorithm and neural network–genetic algorithm for the optimization of quality characteristics during CO2 laser cutting of aluminium alloy

Abstract: Aerospace and automobile industries employ aluminium alloys due to its lightweight and excellent resistance to corrosion. As aluminium is highly reflective and highly thermally conductive, it is difficult-to-cut material by laser processing. The quality characteristics of the cut predominantly depend upon the combination of laser processing parameters. The main quality indices for evaluating CO2 laser cutting were surface roughness, kerf width and kerf taper; and the machining parameters considered were laser power, speed and gas pressure. This work suggests hybrid artificial neural network (ANN)–particle swarm optimization (PSO) algorithm and artificial neural network (ANN)–genetic algorithm (GA) to optimize the associated multi-response characteristics during CO2 laser cutting of aluminium 6061 alloys. The results illustrate that the hybrid ANN–GA and ANN–PSO model is an efficient tool for the optimization of process parameters in CO2 laser cutting of difficult-to-cut material—aluminium. From the optimization results, it can be concluded that the proposed ANN–GA approach can be efficiently utilized to optimize the parameters for obtaining minimum roughness, kerf width and kerf taper.

Control Performance Standard based Load Frequency Control of a two area Reheat Interconnected Power System considering Governor Dead Band nonlinearity using Fuzzy Neural Network

Abstract: The frequency control of reheat interconnected two area power systems are mainly characterized by non-linearity and uncertainty. A hybrid neural network and fuzzy control is proposed for load frequency control in the power systems considering governor dead band (GDB) non-linearity. Fuzzy with neural network is employed to forecast the control input requirement and system's future output, based on the current Area Control Error (ACE) and the predicted change-of-ACE. The Control Performance Standard (CPS) criterion is adopted to the fuzzy controller design, thus improves the dynamic quality of system. The system was simulated and the output responses of frequency deviations in area 1 and area 2 and tie-line power deviations for 1% step-load disturbance in area 1 were obtained. The comparison of frequency deviations and tie-line power deviations for the two area interconnected thermal power system considering GDB nonlinearity with Redox Flow Batteries (RFB) reveals that the system with hybrid fuzzy neural controller enhances a better stability than that of system with integral controller.

Estimation of minimum horizontal stress, geomechanical modeling and hybrid neural network based on conventional well logging data – a case study

Abstract: The minimum horizontal stress (Shmin) is one of the three principal stresses and is required for evaluation of the hydraulic fracturing, sand production, and well stability. Shmin is obtained using direct methods such as the leak-off and mini-frac tests or using some equations like the poroelastic equation. These equations require some information including the elastic parameters, shear sonic logs, core data and the pore pressure. In this study, a geomechanical model is constructed to obtain the minimum horizontal stress; then, an artificial neural network (ANN) with multilayer perceptron and feedforward backpropagation algorithm based on the conventional well logging data is applied to predict the Shmin. Cuckoo optimization algorithm (COA), imperialist competitive algorithm, particle swarm optimization and genetic algorithm are also utilized to optimize the ANN. The proposed methodology is applied in two wells in the reservoir rock located at the southwest of Iran, one for training, and the other...

Compressing and improving fuzzy rules using genetic algorithm and its application to fault detection

Abstract: The fuzzy rule sets, which have been derived from the hybrid neural network model, called the O-EGART-PR-FIS, is an integration of the Adaptive Resonance Theory (ART) into Generalized Regression Neural Network (GRNN), display substantial redundancy and low interpretability that leads to time-consuming prediction process. The O-EGART-PR-FIS approach can achieve the highest accuracy rate among all, however the extracted rules are less compact. Hence, in this paper, we propose a genetic algorithm based method with the inclusion of the “Don't Care” antecedent (hereafter denoted as DC-GA) to the foundation of the O-EGART-PR-FIS, with the aim of further optimizing the existing fuzzy rules. The improved model is applied to two benchmark problems, and the rules extracted are analyzed, discussed and compared with other published methods. From the comparison results, it is observed that the improved model is attested to be statistically superior to other ANN models. Therefore, it reveals the efficacy of DC-GA in eliciting a set of compact and yet easily comprehensible rules while sustaining a high classification performance.