Stock Index Prediction Based on Multi-Level Transfer Function Quantum Neural Tree

2013 ◽  
Vol 457-458 ◽  
pp. 1102-1106
Author(s):  
Hao Teng ◽  
Shu Hui Liu ◽  
Yue Hui Chen
Keyword(s):  
Transfer Function ◽  
Function Approximation ◽  
Predictive Accuracy ◽  
Stock Index ◽  
Accuracy Rate ◽  
Tree Model ◽  
Swarm Optimization ◽  
Multi Level ◽  
Hidden Layer ◽  
And Function

The FlexibleNeural Tree uses a tree structure coding and has excellent predictiveability and function approximation capabilities. Due to it, a quantum neural tree model ispresented based on the multi-level transfer function quantum neuralnetwork and Flexible Neural Tree. In the new model, based on the structure of FlexibleNeural Tree, the transfer function of hidden layer quantum neurons is insteadof multiple superposition oftraditional transfer function, makes the model has a kind of inherent ambiguity.This paper used the improved neural tree asprediction model, particle swarm optimization to optimize the parameters of neuraltree, used probabilistic incremental program evolution to optimizethe structure of neural tree. The experiment result for stock index predictionshows the now method can improve the predictive accuracy rate

Decorrelated Hebbian Learning for Clustering and Function Approximation

1995 ◽  
Vol 7 (2) ◽  
pp. 338-348 ◽  
Author(s):  
G. Deco ◽  
D. Obradovic
Keyword(s):  
Cost Function ◽  
Function Approximation ◽  
Hebbian Learning ◽  
Clustering Problem ◽  
New Learning ◽  
Hidden Layer ◽  
And Function ◽  
The Cost ◽  
Winner Take All ◽  
Gaussian Network

This paper presents a new learning paradigm that consists of a Hebbian and anti-Hebbian learning. A layer of radial basis functions is adapted in an unsupervised fashion by minimizing a two-element cost function. The first element maximizes the output of each gaussian neuron and it can be seen as an implementation of the traditional Hebbian learning law. The second element of the cost function reinforces the competitive learning by penalizing the correlation between the nodes. Consequently, the second term has an “anti-Hebbian” effect that is learned by the gaussian neurons without the implementation of lateral inhibition synapses. Therefore, the decorrelated Hebbian learning (DHL) performs clustering in the input space avoiding the “nonbiological” winner-take-all rule. In addition to the standard clustering problem, this paper also presents an application of the DHL in function approximation. A scaled piece-wise linear approximation of a function is obtained in the supervised fashion within the local regions of its domain determined by the DHL. For comparison, a standard single hidden-layer gaussian network is optimized with the initial centers corresponding to the DHL. The efficiency of the algorithm is demonstrated on the chaotic Mackey-Glass time series.

EFFICIENT HIGHER-ORDER NEURAL NETWORKS FOR CLASSIFICATION AND FUNCTION APPROXIMATION

1992 ◽  
Vol 03 (04) ◽  
pp. 323-350 ◽  
Author(s):  
JOYDEEP GHOSH ◽  
YOAN SHIN
Keyword(s):  
Continuous Function ◽  
Function Approximation ◽  
Order Approximation ◽  
Higher Order ◽  
Compact Set ◽  
Fast Learning ◽  
Learning Rates ◽  
Hidden Layer ◽  
And Function ◽  
Higher Order Networks

This paper introduces a class of higher-order networks called pi-sigma networks (PSNs). PSNs are feedforward networks with a single “hidden” layer of linear summing units and with product units in the output layer. A PSN uses these product units to indirectly incorporate the capabilities of higher-order networks while greatly reducing network complexity. PSNs have only one layer of adjustable weights and exhibit fast learning. A PSN with K summing units provides a constrained Kth order approximation of a continuous function. A generalization of the PSN is presented that can uniformly approximate any continuous function defined on a compact set. The use of linear hidden units makes it possible to mathematically study the convergence properties of various LMS type learning algorithms for PSNs. We show that it is desirable to update only a partial set of weights at a time rather than synchronously updating all the weights. Bounds for learning rates which guarantee convergence are derived. Several simulation results on pattern classification and function approximation problems highlight the capabilities of the PSN. Extensive comparisons are made with other higher order networks and with multilayered perceptrons. The neurobiological plausibility of PSN type networks is also discussed.

Clinical utility of Shabda and Sparsha Pareeksha in evaluation of Prakruta and Vaikruta Garbhavastha

2016 ◽  
Vol 1 (2) ◽  
Author(s):  
Priyanka T K ◽  
V.N. K. Usha ◽  
Sucheta Kumari M
Keyword(s):  
Clinical Study ◽  
Clinical Utility ◽  
Neonatal Outcome ◽  
Predictive Accuracy ◽  
Cost Effective ◽  
Well Being ◽  
Prospective Clinical Study ◽  
Clinical Tool ◽  
Accuracy Rate ◽  
Fetal Pathology

Garbha is a conglomeration of biological mass with different strata including consciousness, needs an innovative clinical tool to evaluate its well being, which proves safe, potent, cost-effective and noninvasive. The idea of taking up this study was to sensitively predict the Prakrutavastha or well being w.r.t Garbha-pushti and ongoing Fetal Pathology, Vaikrutavastha w.s.r Garbhavyapads for a sharp interference to get a possible best neonatal outcome. The objective of this study was to calculate the predictive accuracy of evaluation of Garbhaspandanam on external Shabda and Sparsha Pareeksha. A Prospective Clinical study of Garbhaspandanam (FHS and FM) with external Shabda and Sparsha stimulation on maternal abdomen, from 24th week onwards was conducted in a cohort of 30 Singleton Pregnant women at Dept. of Prasuti Tantra and Stri Roga, S.D.M.C.A. Hospital, Udupi. Among the 9 cases in abnormal category, 2 cases had gone for IUD and one case though placed in abnormal category had responded relatively well to Shabda and Sparsha Pareeksha which may be due to the proper antenatal care and intervention given along with the patient’s Vatakara Nidana Parivarjana. Predictive Accuracy Rate on Shabda and Sparsha Pareeksha showed, FHS 70%, FM 76.7%; FHS 73.3%, FM 66.7% respectively. Shabda and Sparshapareeksha can be utilized as the Garbha - chetana - dyodakalakshana and can be performed as a routine antenatal bedside procedure, which can fairly detect the Prakruta and Vaikrutavastha of Garbha w.r.t Pushti. However larger prospective studies are required.

AUTOMATIC DEVELOPMENT OF FUZZY MEMBERSHIP FUNCTIONS ON HEPATITIS PATIENTS DATA USING PARTICLE SWARM OPTIMIZATION (PSO)

2015 ◽  
Vol 77 (22) ◽  
Author(s):  
Candra Dewi ◽  
Ratna Putri P.S ◽  
Indriati Indriati
Keyword(s):  
Particle Swarm Optimization ◽  
Membership Function ◽  
Fuzzy System ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Membership Functions ◽  
Swarm Optimization ◽  
Global Weight ◽  
The Status ◽  
And Function

Information about the status of disease (prognosis) for patients with hepatitis is important to determine the type of action to stabilize and cure this disease. Among some system, fuzzy system is one of the methods that can be used to obtain this prognosis. In the fuzzification process, the determination of the exact range of membership function will influence the calculation of membership degree and of course will affect the final value of fuzzy system. This range and function can usually be formed using intuition or by using an algorithm. In this paper, Particle Swarm Optimization (PSO) algorithm is implemented to form the triangular membership functions in the case of patients with hepatitis. For testing process, this paper conducts four scenarios to find the best combination of PSO parameter values . Based on the testing it was found that the best parameters to form a membership function range for the hepatitis data is about 0.9, 0.1, 2, 2, 100, 500 for inertia max, inertia min, local ballast constant, global weight constant, the number of particles, and maximum iterations respectively.  

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