Some results from a method for generating recurrent neural networks (RNN) for prediction of financial and macroeconomic time series are presented. In the presented method, a feedforward neural network (FFNN) is first obtained using backpropagation. While backpropagation is usually able to find a fairly good predictor, all FFNN are limited by their lack of short-term dynamic memory. RNNs, by con...

Priyanka Agrawal student, electrical, mits, rgpv, gwalior, mp 474005, india† Dr. A. K. Wadhwani professor, electrical ,mits, rgpv gwalior, mp 474005, india Abstract : This paper deals with the designing of feed forward neural network (FFNN) with the effect of ANN parameters for feature extraction of ECG signal by employing wavelet decomposition. Extraction of ECG features has a significance rol...

Abstract-This paper investigates the ability of feed-forward neural network (FFNN) classifiers trained with examples to generalize and estimate the structure of the feature space in the form of class membership information. A functional theory of FFNN classifiers is developed from formal definitions. The properties of discriminant functions learned by FFNN classifiers from sample data are also ...

In this paper, we investigate the robustness of Feed Forward Neural Network (FFNN) ensemble models applied to quarterly time series forecasting tasks, by comparing their prediction ability with that of Seasonal Auto-regressive Integrated Moving Average (SARIMA) models. We obtained adequate SARIMA models which required statistical knowledge and considerable effort. On the other hand, FFNN ensemb...

artificial neural networks have the advantages such as learning, adaptation, fault-tolerance, parallelism and generalization. this paper mainly intends to offer a novel method for finding a solution of a fuzzy equation that supposedly has a real solution. for this scope, we applied an architecture of fuzzy neural networks such that the corresponding connection weights are real numbers. the sugg...

Artificial Neural Networks (ANNs) have been found to be a robust tool to model many non-linear hydrological processes. The present study aims at evaluating the performance of ANN in simulating and predicting ground water levels in the uplands of a tropical coastal riparian wetland. The study involves comparison of two network architectures, Feed Forward Neural Network (FFNN) and Recurrent Neura...

This paper presents a novel edge detector based on Feed-Forward Neural Networks (FFNNs). The FFNN computing architecture has two stages, which is a feature enhancement stage as well as a structural boundary extraction stage. The first stage is a traditional supervised BP network, and the second one is manually designed without training. Experiments based on both synthetic and natural images sho...

Feature extraction methods and subsequent neural network performances are explored in this paper. Object recognition method ‘regionprops’ and moment invariants are used to extract basic characteristics from acquired bloodstain images. The extracted features are in return fed into a neural network for the purpose of pattern recognition. The blood drop in the image is first detected using sobel e...

Unlike feedforward neural networks (FFNN) which can act as universal function approximaters, recursive neural networks have the potential to act as both universal function approximaters and universal system approximaters. In this paper, a globally recursive neural network least mean square (GRNNLMS) gradient descent or a real time recursive backpropagation (RTRBP) algorithm is developed for a s...

In this paper, we show how Bayesian neural networks can be used for time series analysis. We consider a block based model building strategy to model linear and nonlinear features within the time series. A proposed model is a linear combination of a linear autoregression term and a feedforward neural network (FFNN) with an unknown number of hidden nodes. To allow for simpler models, we also cons...