Benchmarking Probabilistic Neural Network Algorithms
نویسنده
چکیده
The progress of research in probabilistic neural network (PNN) and related issues is straight related to directly compare the performance of different PNN algorithm versions. In most cases, the PNN application in real life issues involves the classical activation function (Parzen-Cacoulos estimator) only. Although this estimator has been used in most experimental works so far, it is not the only consistent estimator available for practical purpose. The aim of this paper is to introduce new activation functions and to present a performance benchmark for them, tested with a medical dataset containing information regarding hepatic diseases.
منابع مشابه
An integrated data envelopment analysis–artificial neural network approach for benchmarking of bank branches
Efficiency and quality of services are crucial to today’s banking industries. The competition in this section has become increasingly intense, as a result of fast improvements in Technology. Therefore, performance analysis of the banking sectors attracts more attention these days. Even though data envelopment analysis (DEA) is a pioneer approach in the literature as of an efficiency measurement...
متن کاملNovel Radial Basis Function Neural Networks based on Probabilistic Evolutionary and Gaussian Mixture Model for Satellites Optimum Selection
In this study, two novel learning algorithms have been applied on Radial Basis Function Neural Network (RBFNN) to approximate the functions with high non-linear order. The Probabilistic Evolutionary (PE) and Gaussian Mixture Model (GMM) techniques are proposed to significantly minimize the error functions. The main idea is concerning the various strategies to optimize the procedure of Gradient ...
متن کاملDesigning of a New Transformer Ground Differential Relay Based on Probabilistic Neural Network
Low- impedance transformer ground differential relay is a part of power transformer protection system that is employed for detecting the internal earth faults. This is a fast and sensitive relay, but during some external faults and inrush current conditions, may be exposed to maloperation due to current transformer (CT) saturation. In this paper, a new intelligent transformer ground differentia...
متن کاملAn Efficiency Measurement and Benchmarking Model Based on Tobit Regression, GANN-DEA and PSOGA
The purpose of this study is designing a model based on Tobit regression, DEA, Artificial Neural Network, Genetic Algorithm and Particle Swarm Optimization to evaluate the efficiency and also benchmarking the efficient and inefficient units. This model has three stages, and it uses the data envelopment analysis combined model with neural network, optimized by genetic algorithm, to evaluate the ...
متن کاملPersian Handwritten Digit Recognition Using Particle Swarm Probabilistic Neural Network
Handwritten digit recognition can be categorized as a classification problem. Probabilistic Neural Network (PNN) is one of the most effective and useful classifiers, which works based on Bayesian rule. In this paper, in order to recognize Persian (Farsi) handwritten digit recognition, a combination of intelligent clustering method and PNN has been utilized. Hoda database, which includes 80000 P...
متن کامل