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2003•Unpublished venueRequires access

A new method for density estimation by using forward neural network

Feng Xiongfeng, Yang Xianhui, Xu Yongmao

Open publisher page 3 citations

Abstract

A new method for estimation of probability density function by using forward neural network is presented for the implementation of statistical process control. A new neural network estimator of continuous form is proposed. Simulation results illustrate the effectiveness of the proposed estimator. The relationship with other methods of estimation of density function is discussed. The proposed method can be extended to solve two-dimensional or multi-dimensional problems.

About this research paper

What this paper is about

A new method for estimation of probability density function by using forward neural network is presented for the implementation of statistical process control. A new neural network estimator of continuous form is proposed. Simulation results illustrate the effectiveness of the proposed estimator. The relationship with other methods of estimation of density function is discussed. The proposed method can be extended to solve two-dimensional or multi-dimensional problems.

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OpenAlex reports 3 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Method / approach

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Available abstract

A new method for estimation of probability density function by using forward neural network is presented for the implementation of statistical process control. A new neural network estimator of continuous form is proposed. Simulation results illustrate the effectiveness of the proposed estimator. The relationship with other methods of estimation of density function is discussed. The proposed method can be extended to solve two-dimensional or multi-dimensional problems.

Key concepts: Estimator, Artificial neural network, Probability density function, Computer science, Estimation, Density estimation, Process (computing), Function (biology)

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