Applying Neural Networks for Modeling of Financial Assets

The purpose of this chapter is to develop an analytical system for forecasting prices of financial assets with the use of artificial neural networks technology. Proposed by the authors, the analytical system consists of several neural networks, each of which makes the forecast of financial assets prices. The system includes recurrence (with feedback) neural networks with sigmoidal activation formula. This allows the networks to “remember” a sequence of reactions to the same stimulus. The learning process of neural networks is performed using an algorithm of back propagation of error. The key parameters of forecast for this analytical system are the indicators presented by the terminal MetaTrader 4-broker Forex Club: Average Directional и Movement Index; Bollinger Bands; Envelopes; Ichimoku Kinko Hyo; Moving Average; Parabolic SAR; Standard Deviation; Average True Range; and others.

A Systemic Study of Pattern Recognition System Using Feedback Neural Networks

The evolution of artificial intelligence has led to the developments of various smart applications such as the pattern recognition models. Pattern recognition techniques has as widely applied in many real life applications such character recognition, speech recognition, and bio-metric authentication as well person identification. In this paper, we report on the detailed design of pattern recognition system using Hopfield Feedback Neural Network (HFNN) with the least possible recognition error. As a case study, we have applied the proposed HFNN model to recognize the decimal digits 0 - 9 where each image digit comprises a 12x10 pixels. The developed HFNN model has been efficiently used in recognizing the patterns with 20% random bit noise at maximum recognition accuracy. However, to assure the the least possible recognition error, we have trained our HFNN through the digit patterns’ perdition phase of 0% noisy patterns and the system was able to correctly predict all the patterns without any bit error. Finally, we have plotted all output patterns including the desired patterns, the training patterns, the 20% noisy patterns and recognized patterns, for comparison purposes and to gain more insights about the accuracy achieved by applying the proposed HFNN.

Applying Neural Networks for Modeling of Financial Assets

The purpose of this chapter is to develop an analytical system for forecasting prices of financial assets with the use of artificial neural networks technology. Proposed by the authors, the analytical system consists of several neural networks, each of which makes the forecast of financial assets prices. The system includes recurrence (with feedback) neural networks with sigmoidal activation formula. This allows the networks to “remember” a sequence of reactions to the same stimulus. The learning process of neural networks is performed using an algorithm of back propagation of error. The key parameters of forecast for this analytical system are the indicators presented by the terminal MetaTrader 4-broker Forex Club: Average Directional и Movement Index; Bollinger Bands; Envelopes; Ichimoku Kinko Hyo; Moving Average; Parabolic SAR; Standard Deviation; Average True Range; and others.

CLASSIFICATION OF HEARING PERCEPTION LEVEL USING AUDITORY EVOKED POTENTIALS

An auditory loss is one of the most common disabilities present in newborns and infants in India. A conventional hearing screening test’s applicability is limited as it requires a feedback response from the subject under test. To overcome such problems, the primary focus of this study is to develop an auditory loss assessment system using auditory evoked potential signals (AEP). The AEP responses of fourteen normal hearing subjects to auditory stimuli (20 dB, 30 dB, 40 dB, 50 dB and 60 dB) were derived from electroencephalogram (EEG) recordings. Box counting fractal method is applied to extract the fractal features from the recorded AEP signals. Feed forward and feedback neural networks are employed to distinguish the different hearing perception levels. The performance of the proposed auditory loss assessment system found to exceed 80% accuracy. This study indicates that AEP responses to the auditory stimuli to the normal hearing persons can clearly distinguish the higher order auditory stimuli followed by the lower order auditory stimuli and it can be used to estimate the level of hearing loss in the patient.