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 Hybrid Neural Network and Traditional Approach for Forecasting Lumpy Demand

Abstract: Accurate demand forecasting is one of the most key issues in inventory management of spare parts. The problem of modeling future consumption becomes especially difficult for lumpy patterns, which characterized by intervals in which there is no demand and, periods with actual demand occurrences with large variation in demand levels. However, many of the forecasting methods may perform poorly when demand for an item is lumpy. In this study based on the characteristic of lumpy demand patterns of spare parts a hybrid forecasting approach has been developed, which use a multi-layered perceptron neural network and a traditional recursive method for forecasting future demands. In the described approach the multi-layered perceptron are adapted to forecast occurrences of non-zero demands, and then a conventional recursive method is used to estimate the quantity of non-zero demands. In order to evaluate the performance of the proposed approach, their forecasts were compared to those obtained by using Syntetos & Boylan approximation, recently employed multi-layered perceptron neural network, generalized regression neural network and elman recurrent neural network in this area. The models were applied to forecast future demand of spare parts of Arak Petrochemical Company in Iran, using 30 types of real data sets. The results indicate that the forecasts obtained by using our proposed mode are superior to those obtained by using other methods

Water chiller fault diagnosis method based on hybrid neural network

Abstract: The invention discloses a water chiller fault diagnosis method based on a hybrid neural network, and aims at the problem that the single BP neural network is difficult to perform accurate prediction of the water chiller and the BP neural network has its own disadvantages. According to the method, a hybrid neural network model RBF-BP based on wavelet transform is put forward to perform fault diagnosis of the water chiller through combination of the characteristic that RBF neural network can approximate any function. The RBF network and the BP network are connected in parallel as one neural network, which is called the RBF-BP implicit strata for short. The algorithm has the advantages of both the RBF network and the BP network. Its learning process converges quickly and avoids the problem that the training process is liable to fall into the local minimum value. The method can be effectively applied to fault diagnosis of the water chiller and improve the performance of fault diagnosis.

Implementation of a neural network processor based on RISC architecture for various signal processing applications

Abstract: In this paper, hybrid neural network processor (HANNP) is designed in VLSI. The HANNP has RISC based architecture leading to an effective general digital signal processing and artificial neural networks computation. The architecture of a HANNP including the general digital processing units such as 64-bit floating-point arithmetic unit (FPU), a control unit (CU) and neural network processing units such as artificial neural computing unit (NNPU), specialized neural data bus and interface unit, etc. The HANNP is modeled in Veilog HDL and implemented with FPGA. Character recognition problems and Kohonen self-organization problems are applied to the proposed HANNP to justify its applicability to real engineering problems.