This paper examines the e ectiveness of using a quasi-Newton based training of a feedforward neural network for forecasting. We have developed a novel quasi-Newton based training algorithm using a generalized logistic function. We have shown that a well designed feed forward structure can lead to a good forecast without the use of the more complicated feedback/feedforward structure of the recur...

The problem of short-term electric load forecasting (STLF) is considered. A modified architecture of Elman-type recurrent neural network is proposed. It utilizes a special fuzzification layer to deal with quantitative as well as ordinal and nominal data. The second hidden layer of the network consists of standard Rosenblatt-type neurons with sigmoidal activation functions. The context layer is ...

Financial forecasting is an example of a signal processing problem w hich is challenging due to Small sizes, high noise, nonstationarity, and non-linearity,but fast forecasting of stock market price is very important for strategic business planning.Present study is aimed to develop a comparative predictive model w ith Feedforward Multilayer Artif icial Neural Netw ork & Recurrent Time Delay Neu...

in the regulated nakdong river, algal proliferations are annually observed in some seasons, with cyanobacteria (microcystis aeruginosa) appearing in summer and diatom blooms (stephanodiscus hantzschii) in winter. this study aims to develop two ecological models forecasting future chlorophyll a at two time-steps (one-week and one-year forecasts), using recurrent neural networks tuned by genetic...

Rural regions rely heavily on agriculture for their economic survival. Therefore, it is crucial farmers to implement effective and technical solutions raise production, lessen the impact of issues associated animal husbandry, improve agricultural yields. Because technological developments in computers data storage, huge volumes information are now available. The difficulty extracting useful fro...

Evolutionary training methods for Artificial Neural Networks can escape local minima. Thus, they are useful to train recurrent neural networks for short-term weather forecasting. However, these algorithms are not guaranteed to converge fast or even converge at all due to their stochastic nature. In this paper, we present an algorithm that uses implicit gradient information and is able to train ...

There are many models that have been used to simulate the rainfall-runoff relationship. The artificial neural network (ANN) model was selected to investigate an approach of improving daily runoff forecasting accuracy in terms of data preprocessing. Singular spectrum analysis (SSA) as one data preprocessing technique was adopted to deal with the model inputs and the SSA-ANN model was developed. ...

In the context of dam deformation monitoring, prediction task is essentially a time series problem that involves non-stationarity and complex influencing factors. To enhance accuracy predictions address challenges posed by high randomness parameter selection in LSTM models, novel approach called sparrow search algorithm–long short-term memory (SSA–LSTM) has been proposed for predicting concrete...

We present a simple SSA construction algorithm, which allows direct translation from an abstract syntax tree or bytecode into an SSA-based intermediate representation. The algorithm requires no prior analysis and ensures that even during construction the intermediate representation is in SSA form. This allows the application of SSA-based optimizations during construction. After completion, the ...