scispace - formally typeset
Search or ask a question
Topic

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.


Papers
More filters
01 Jan 1998
TL;DR: A new approach for neural network design in solving optimization problems, which is different from the traditional approach is presented and the new hybrid neural network model is introduced to overcome limitations.
Abstract: This effort aims to make contributions in two areas of research. One is the area of neural network design. We present a new approach for neural network design in solving optimization problems, which is different from the traditional approach. Several limitations of traditional neural networks are discussed and the new hybrid neural network model is introduced to overcome those limitations. Neural network as a search technique in optimization has to prove itself not only capable of solving an optimization problem, but also be able to compete with other techniques on performance measures. We focus our study on two performance measures: efficiency and effectiveness. By definition, an efficient search technique can solve problems fast where as an effective technique will provide high quality solutions. Our research is successful in both directions. The second major contribution we seek to make is in the area of processor scheduling. We test our hybrid neural network by solving a processor scheduling problem, and we compare our neural network solutions with the solutions from the existing best heuristics from literature. The hybrid neural network outperforms the heuristics in most cases and is able to improve the solutions from starting with a heuristic solution. The solution time is fairly short, i.e., in the same order of the heuristics. The processor scheduling problem we solved here is the Flexible Flow Shop (FFS) scheduling. The problem is NP-complete, a category o f hard combinatorial optimization problems in literature. Applications of this problem include, but not limited to: computing systems with multiple processors at each phase where all tasks must be done through a series of phases; manufacturing systems with multiple machines at each stage where all jobs need to go through a series of stages. Therefore, solving the FFS problem is not only significant in the theory of optimization, but also in real world applications.

5 citations

Journal ArticleDOI
TL;DR: This paper presents a unified information processing structure by fusing the convolutional or fully connected neural network with the delay feedback reservoir into a hybrid neural network model to accomplish the comprehensive information processing goal.

5 citations

Book ChapterDOI
26 Jun 2003
TL;DR: An artificial neural network (ANN), which estimates the residuals from the most accurate conventional option pricing model, is constrained in such a way so that pricing must be rational at the option-pricing boundaries.
Abstract: It is well known that conventional option pricing models have systematic, statistically and economically significant errors or residuals. In this work an artificial neural network (ANN), which estimates the residuals from the most accurate conventional option pricing model, so as to improve option pricing accuracy, is constrained in such a way so that pricing must be rational at the option-pricing boundaries. These constraints lead to statistically and economically significant out-performance relative to both the most accurate conventional and non-constrained ANN option pricing models.

5 citations

Journal ArticleDOI
TL;DR: An approach to optimize window length for SSA and number of decomposition stages for wavelet transform applied in a rainfall-runoff model is addressed and a hybrid neural network is developed to take the advantage of wavelet and SSA-based ANN models.
Abstract: The proper function of artificial neural networks (ANNs) depends on several factors including the suitability of input variables and the amount of information they can add to the model in order to produce the required target output(s). Wavelet transforms and to lesser extent singular spectrum analysis (SSA) are well known and widely applied pre-processing methods to enhance ANN models. An important step in the SSA algorithm and wavelet transform method is choosing the window length (L) and determining the suitable number of decomposition stages, respectively. In most past research, these parameters have been used as granted. Moreover, a research to show the impact of using a combination of wavelet and SSA is absent. This study addresses an approach to optimize window length for SSA and number of decomposition stages for wavelet transform applied in a rainfall-runoff model. Moreover, a hybrid neural network is developed to take the advantage of wavelet and SSA-based ANN models. The results show a s...

5 citations


Network Information
Related Topics (5)
Artificial neural network
207K papers, 4.5M citations
89% related
Feature extraction
111.8K papers, 2.1M citations
88% related
Fuzzy logic
151.2K papers, 2.3M citations
85% related
Convolutional neural network
74.7K papers, 2M citations
84% related
Deep learning
79.8K papers, 2.1M citations
83% related
Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20233
20228
2021128
2020119
2019104
201863