Linear and nonlinear ARMA model parameter estimation using an artificial neural network.

K H Chon; R J Cohen

doi:10.1109/10.554763

Home/Archive/PubMed

PubMed · 9216130

Linear and nonlinear ARMA model parameter estimation using an artificial neural network.

K H Chon·R J Cohen

View original source ↗Export metadata ↓

Abstract

This paper addresses parametric system identification of linear and nonlinear dynamic systems by analysis of the input and output signals. Specifically, we investigate the relationship between estimation of the system using a feedforward neural network model and estimation of the system by use of linear and nonlinear autoregressive moving-average (ARMA) models. By utilizing a neural network model incorporating a polynomial activation function, we show the equivalence of the artificial neural network to the linear and nonlinear ARMA models. We compare the parameterization of the estimated system using the neural network and ARMA approaches by utilizing data generated by means of computer simulations. Specifically, we show that the parameters of a simulated ARMA system can be obtained from the neural network analysis of the simulated data or by conventional least squares ARMA analysis. The feasibility of applying neural networks with polynomial activation functions to the analysis of experimental data is explored by application to measurements of heart rate (HR) and instantaneous lung volume (ILV) fluctuations.

Explore related subjects

Computer Simulation Electrocardiography Heart Rate Humans Least-Squares Analysis Linear Models Lung Volume Measurements Models, Biological Neural Networks, Computer Nonlinear Dynamics Supine Position NASA Discipline Regulatory Physiology Non-NASA Center Comparative Study Journal Article Research Support, U.S. Gov't, Non-P.H.S.Research Support, U.S. Gov't, P.H.S.

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

K H Chon, R J Cohen. 1997. Linear and nonlinear ARMA model parameter estimation using an artificial neural network.. https://doi.org/10.1109/10.554763

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2025

Generating correlated data for omics simulation.

Simulation of realistic omics data is a key input for benchmarking studies that help users obtain optimal computational pipelines. Omics data involves large numbers of measured features on each sample and these measures are generally correlated with each other. However, simulation too often ignores these correlations, perhaps due to computational and statistical hurdles of doing so. To alleviate this, we describe three approaches for generating omics-scale data with correlated measures which mimic real datasets. These approaches are all based on a Gaussian copula approach with a covariance matrix that decomposes into a diagonal part and a low-rank part. This decomposition allows for extremely efficient simulation, overcoming a hurdle for adoption of past methods. We use these approaches to demonstrate the importance of including correlation in two benchmarking applications. First, we show that variance of results from the popular DESeq2 method increases when dependence is included. Second, we demonstrate that CYCLOPS, a method for inferring circadian time of collection from transcriptomics, improves in performance when given gene-gene dependencies in some circumstances. We provide an R package, dependentsimr, that has efficient implementations of these methods and can generate dependent data with arbitrary marginal distributions, including discrete (binary, ordered categorical, Poisson, negative binomial), continuous (normal), or with an empirical distribution.

Computer Simulation↗

PubMed2017

Addressing current challenges in cancer immunotherapy with mathematical and computational modelling.

The goal of cancer immunotherapy is to boost a patient's immune response to a tumour. Yet, the design of an effective immunotherapy is complicated by various factors, including a potentially immunosuppressive tumour microenvironment, immune-modulating effects of conventional treatments and therapy-related toxicities. These complexities can be incorporated into mathematical and computational models of cancer immunotherapy that can then be used to aid in rational therapy design. In this review, we survey modelling approaches under the umbrella of the major challenges facing immunotherapy development, which encompass tumour classification, optimal treatment scheduling and combination therapy design. Although overlapping, each challenge has presented unique opportunities for modellers to make contributions using analytical and numerical analysis of model outcomes, as well as optimization algorithms. We discuss several examples of models that have grown in complexity as more biological information has become available, showcasing how model development is a dynamic process interlinked with the rapid advances in tumour-immune biology. We conclude the review with recommendations for modellers both with respect to methodology and biological direction that might help keep modellers at the forefront of cancer immunotherapy development.

Computer Simulation↗

PubMed2000

The inhibition of Escherichia coli lac operon gene expression by antigene oligonucleotides-mathematical modeling.

Gene transcription is regulated by transcription factors that can bind to specific regions on DNA. Antigene oligonucleotides (oligos) can bind to specific regions on DNA and form a triplex with the double-stranded DNA. The triplex can competitively inhibit the binding of transcription factors and, as a result, transcription can be inhibited. A genetically structured model has been developed to quantitatively describe the inhibition of the Escherichia coli lac operon gene expression by triplex-forming oligos. The model predicts that the effect of triplex-forming oligos on the lac operon gene expression depends on their target sites. Oligonucleotides targeted to the operator are much more effective than those targeted to other regulatory sites on the lac operon. In some cases, the effect of oligo binding is similar to that of a mutation in the lac operon. The model provides insight as to the specific binding site to be targeted to achieve the most effective inhibition of gene expression. The model is also capable of predicting the oligo concentration needed to inhibit gene expression, which is in general agreement with results reported by other investigators.

Computer Simulation↗