Probabilistic, Recurrent, Fuzzy Neural Network for Processing Noisy Time-Series Data

Time Series Data Analysis Using Probabilistic and Neural Network

Artificial intelligence decision support system is always a popular topic in providing the human user with an optimized decision recommendation when operating under uncertainty in complex environments. The particular focus of our discussion is to compare different methods of artificial intelligence decision support systems in the investment domain – the goal of investment decision-making is to ...

Fuzzy Temporal Sequence Processing By Fuzzified Recurrent Neural Fuzzy Network

Abstrad A Fuzzijed TSK-type Recurrent Neiiral Fuzzy Network (FTRNFN) for handling furry temporal information is proposed in this paper. The inputs and oulputs of FTRNFN are fuzzy pattems represented by Gaussian or isosceles triangular membership functions. In structure, FTRNFN is a recurrent fizzy nefwork constructed from a series of recurrent fuzzy +then rules with TSK-t)pe Consequent parts. T...

Noisy Time Series Prediction using a Recurrent Neural Network and Grammatical Inference

Financial forecasting is an example of a signal processing problem which is challenging due to small sample sizes, high noise, non-stationarity, and non-linearity. Neural networks have been very successful in a number of signal processing applications. We discuss fundamental limitations and inherent difficulties when using neural networks for the processing of high noise, small sample size sign...

Multi-Context Recurrent Neural Network for Time Series Applications

this paper presents a multi-context recurrent network for time series analysis. While simple recurrent network (SRN) are very popular among recurrent neural networks, they still have some shortcomings in terms of learning speed and accuracy that need to be addressed. To solve these problems, we proposed a multi-context recurrent network (MCRN) with three different learning algorithms. The perfo...

Signal processing and neural network models for economic time series