4,046 research outputs found
PSO based Neural Networks vs. Traditional Statistical Models for Seasonal Time Series Forecasting
Seasonality is a distinctive characteristic which is often observed in many
practical time series. Artificial Neural Networks (ANNs) are a class of
promising models for efficiently recognizing and forecasting seasonal patterns.
In this paper, the Particle Swarm Optimization (PSO) approach is used to
enhance the forecasting strengths of feedforward ANN (FANN) as well as Elman
ANN (EANN) models for seasonal data. Three widely popular versions of the basic
PSO algorithm, viz. Trelea-I, Trelea-II and Clerc-Type1 are considered here.
The empirical analysis is conducted on three real-world seasonal time series.
Results clearly show that each version of the PSO algorithm achieves notably
better forecasting accuracies than the standard Backpropagation (BP) training
method for both FANN and EANN models. The neural network forecasting results
are also compared with those from the three traditional statistical models,
viz. Seasonal Autoregressive Integrated Moving Average (SARIMA), Holt-Winters
(HW) and Support Vector Machine (SVM). The comparison demonstrates that both
PSO and BP based neural networks outperform SARIMA, HW and SVM models for all
three time series datasets. The forecasting performances of ANNs are further
improved through combining the outputs from the three PSO based models.Comment: 4 figures, 4 tables, 31 references, conference proceeding
Metaheuristic design of feedforward neural networks: a review of two decades of research
Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era
Neural Network based Electron Identification in the ZEUS Calorimeter
We present an electron identification algorithm based on a neural network
approach applied to the ZEUS uranium calorimeter. The study is motivated by the
need to select deep inelastic, neutral current, electron proton interactions
characterized by the presence of a scattered electron in the final state. The
performance of the algorithm is compared to an electron identification method
based on a classical probabilistic approach. By means of a principle component
analysis the improvement in the performance is traced back to the number of
variables used in the neural network approach.Comment: 20 pages, latex, 16 figures appended as uuencoded fil
Fixed-Point Performance Analysis of Recurrent Neural Networks
Recurrent neural networks have shown excellent performance in many
applications, however they require increased complexity in hardware or software
based implementations. The hardware complexity can be much lowered by
minimizing the word-length of weights and signals. This work analyzes the
fixed-point performance of recurrent neural networks using a retrain based
quantization method. The quantization sensitivity of each layer in RNNs is
studied, and the overall fixed-point optimization results minimizing the
capacity of weights while not sacrificing the performance are presented. A
language model and a phoneme recognition examples are used
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