New Radial Basis Function Interior Point Method Learning Rule for Rough Set Reduct Detection

Information and communication technology made shopping more convenient for common man. Additionally, customers compare both online and offline price of a commodity. For this reason, offline shopping markets think of customer satisfaction and try to attract customers by various means. But, prediction of customer's choice in an information system is a major issue today. Much research is carried out in this direction for single universe. But, in many real life applications it is observed that relation is established between two universes. To this end, in this paper the authors propose a model to identify customer choice of super markets using fuzzy rough set on two universal sets and radial basis function neural network. The authors use fuzzy rough set on two universal sets on sample data to arrive at customer choice of super markets. The information system with customer choice is further trained with radial basis function neural network for identification of customer choice of supermarkets when customer size increases. A real life problem is presented to show the sustainability of the proposed model.

Download Full-text

An Integrated Model of Rough Set and Radial Basis Function Neural Network for Early Warning of Enterprise Human Resource Crisis

International Journal of Fuzzy Systems ◽

10.1007/s40815-019-00758-z ◽

2019 ◽

Vol 21 (8) ◽

pp. 2462-2471 ◽

Cited By ~ 1

Author(s):

Gang Li

Keyword(s):

Neural Network ◽

Radial Basis Function ◽

Human Resource ◽

Rough Set ◽

Early Warning ◽

Basis Function ◽

Integrated Model ◽

Radial Basis ◽

Human Resource Crisis

Download Full-text

Probabilistic Winner-Take-All Learning Algorithm for Radial-Basis-Function Neural Classifiers

Neural Computation ◽

10.1162/neco.1994.6.5.927 ◽

1994 ◽

Vol 6 (5) ◽

pp. 927-943 ◽

Cited By ~ 8

Author(s):

Hossam Osman ◽

Moustafa M. Fahmy

Keyword(s):

Radial Basis Function ◽

Basis Function ◽

Learning Algorithm ◽

Learning Rule ◽

Competitive Learning ◽

Experimental Comparison ◽

Radial Basis ◽

Hidden Layer ◽

Winner Take All ◽

Take All

This paper proposes a new adaptive competitive learning algorithm called “the probabilistic winner-take-all.” The algorithm is based on a learning scheme developed by Agrawala within the statistical pattern recognition literature (Agrawala 1970). Its name stems from the fact that for a given input pattern once each competitor computes the probability of being the one that generated this pattern, the computed probabilities are utilized to probabilistically choose a winner. Then, only this winner is permitted to learn. The learning rule of the algorithm is derived for three different cases. Its properties are discussed and compared to those of two other competitive learning algorithms, namely the standard winner-take-all and the maximum-likelihood soft competition. Experimental comparison is also given. When all three algorithms are used to train the hidden layer of radial-basis-function classifiers, experiments indicate that classifiers trained with the probabilistic winner-take-all outperform those trained with the other two algorithms.

Download Full-text

Online Learning in Radial Basis Function Networks

Neural Computation ◽

10.1162/neco.1997.9.7.1601 ◽

1997 ◽

Vol 9 (7) ◽

pp. 1601-1622 ◽

Cited By ~ 28

Author(s):

Jason A. S. Freeman ◽

David Saad

Keyword(s):

Radial Basis Function ◽

Basis Function ◽

Learning Rule ◽

Generalization Error ◽

Internal Dynamics ◽

Average Case ◽

Radial Basis ◽

Time Required ◽

Insight Into

An analytic investigation of the average case learning and generalization properties of radial basis function (RBFs) networks is presented, utilizing online gradient descent as the learning rule. The analytic method employed allows both the calculation of generalization error and the examination of the internal dynamics of the network. The generalization error and internal dynamics are then used to examine the role of the learning rate and the specialization of the hidden units, which gives insight into decreasing the time required for training. The realizable and some over realizable cases are studied in detail: the phase of learning in which the hidden units are unspecialized (symmetric phase) and the phase in which asymptotic convergence occurs are analyzed, and their typical properties found. Finally, simulations are performed that strongly confirm the analytic results.

Download Full-text