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.

Surface roughness prediction using hybrid neural networks

Abstract: Surface roughness is an important outcome in the machining process and it forms a major part in the manufacturing system. Surface roughness depends on different machining parameters and its prediction and control is a challenge to the researchers. There is a need to predict surface roughness prior to machining to attain higher productivity levels. Owing to advances in computing power there is an increase in the demand for the use of intelligent techniques. Recent research is directed towards hybridization of intelligent techniques to make the best out of each technique. This article proposes the development of a novel hybrid Neural Network (NN) trained with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for the prediction of surface roughness. The proposed hybrid neural network is found to be competent in terms of computational speed and efficiency over the neural network model.

A Hybrid Neural Network and ARIMA Model for Energy Consumption Forcasting

Abstract: Energy consumption time series consist of complex linear and non-linear patterns and are difficult to forecast. Neither autoregressive integrated moving average (ARIMA) nor artificial neural networks (ANNs) can be adequate in modeling and predicting energy consumption. The ARIMA model cannot deal with nonlinear relationships while the neural network model alone is not able to handle both linear and nonlinear patterns equally well. In the present study, a hybrid methodology that combines both ARIMA and ANN models is proposed to take advantage of the unique strength of ARIMA and ANN models in linear and nonlinear modeling. The empirical results with energy consumption data of Hebei province in China indicate that the hybrid model can be an effective way to improve the energy consumption forecasting accuracy obtained by either of the models used separately

An adaptive neural network topology for degradation compensation of thin film tin oxide gas sensors

Abstract: A hybrid neural network for gas sensing application is presented, which is based on adaptive resonance theory. The network may use as an input one or more gas sensors. The basic feature of the proposed topology is its ability to learn a new pattern or form a new pattern category at any point of its operation. At the same time it retains knowledge of previously learned patterns or pattern categories. This adaptation ability helps the network to solve many of the problems encountered with tin oxide gas sensors, like instabilities and degradation. The functionality of the network is presented in the two cases of one and four input providing gas sensors. The experimental results show that the effect of sensor degradation maybe compensated by the proposed network topology.

Prediction of flow stress for carbon steels using recurrent self-organizing neuro fuzzy networks

Abstract: Mechanical properties of any material are extensively influenced by the parameters such as strain, strain rate, temperature, and its composition. The characteristics of any material such as ductility, strain hardening, strength, dynamic recovery, grain growth, and recrystallization are greatly affected by the influence of various process parameters. So, it is essential to have the knowledge of the constitutive relationships that relate different process variables to flow stress of the deforming material which estimates various parameters such as load, energy, and stress in the metal forming operations. A consistent effort has been gone into developing the constitutive equations for the detailed mathematical description of the flow curves and the aforementioned parameters for years now. Soft computing tools that concern computation of an imprecise environment and model very complex systems those are based on input-output relationship have gained significant attention in recent years. The intricacies of the mathematical modeling of the mechanical properties of the material, enticed the artificial research community to take this as a challenge. One such soft computing tool neural network is applied in this field to predict the behavior accurately. In this paper, a hybrid neural network model better known as RSONFN (Recurrent Self-Organizing Neural Network Model) is applied to predict the flow stress for carbon steels. The RSONFN is having the advantages of the well-established technologies of the artificial intelligence tools such as Fuzzy logic to capture long range data sets and neural networks. The RSONFN structure is a dynamic one as the numbers of its layers as well as the numbers of nodes in each layer of the network are not predetermined. Such an attribute differentiate it from the Multilayer perceptron which is having static structure. The results obtained by this network prove its superiority over other existing tools.

A new method of emotional analysis based on CNN–BiLSTM hybrid neural network

Abstract: The hybrid neural network model proposed in this paper consists of two main parts: extracting local features of text vectors by convolutional neural network, extracting global features related to text context by BiLSTM, and fusing the features extracted by the two complementary models. In this paper, the pre-processed sentences are put into the hybrid neural network for training. The trained hybrid neural network can automatically classify the sentences. When testing the algorithm proposed in this paper, the training corpus is Word2vec. The test results show that the accuracy rate of text categorization reaches 94.2%, and the number of iterations is 10. The results show that the proposed algorithm has high accuracy and good robustness when the sample size is seriously unbalanced.