Identification and prediction of non-linear models with recurrent neural network

1993 ◽  
pp. 531-535 ◽  
Author(s):  
Adam Olivier ◽  
Zarader Jean-Luc ◽  
Milgram Maurice
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Linear Models ◽  
Non Linear

Neural Network Subtyping of Depression

1998 ◽  
Vol 32 (5) ◽  
pp. 687-694 ◽  
Author(s):  
Tony M. Florio ◽  
Gordon Parker ◽  
Marie-Paule Austin ◽  
Ian Hickie ◽  
Philip Mitchell ◽  
...  
Keyword(s):  
Neural Network ◽  
Clinical Decision Making ◽  
Linear Models ◽  
Function Analysis ◽  
Clinical Decision ◽  
Linear Analysis ◽  
Linear Discriminant ◽  
Non Linear ◽  
Depressive Subtypes ◽  
Definition Of

Objective: To examine the applicability of a neural network classification strategy to examine the independent contribution of psychomotor disturbance (PMD) and endogeneity symptoms to the DSM-III-R definition of melancholia. Method: We studied 407 depressed patients with the clinical dataset comprising 17 endogeneity symptoms and the 18-item CORE measure of behaviourally rated PMD. A multilayer perceptron neural network was used to fit non-linear models of varying complexity. A linear discriminant function analysis was also used to generate a model for comparison with the non-linear models. Results: Models (linear and non-linear) using PMD items only and endogeneity symptoms only had similar rates of successful classification, while non-linear models combining both PMD and symptom scores achieved the best classifications. Conclusions: Our current non-linear model was superior to a linear analysis, a finding which may have wider application to psychiatric classification. Our non-linear analysis of depressive subtypes supports the binary view that melancholic and non-melancholic depression are separate clinical disorders rather than different forms of the same entity. This study illustrates how non-linear modelling with neural networks is a potentially fruitful approach to the study of the diagnostic taxonomy of psychiatric disorders and to clinical decision-making.

scholarly journals HYBRID DECIDING MODULES WITH VIRTUAL STREAMS FOR CLASSIFICATION AND PREDICTION OF FUNCTIONAL STATE OF COMPLEX SYSTEMS

2018 ◽  
Vol 22 (4) ◽  
pp. 123-134 ◽  
Author(s):  
A. V. Kiselev ◽  
T. V. Petrova ◽  
S. V. Degtyaryov ◽  
A. F. Rybochkin ◽  
S. A. Filist ◽  
...  
Keyword(s):  
Neural Network ◽  
Complex Systems ◽  
Functional State ◽  
Latent Variables ◽  
Linear Models ◽  
Simulation Method ◽  
Non Linear ◽  
Intelligent Decision Support Systems ◽  
Decision Module ◽  
Intelligent Decision Support

The problem reviewed of building intelligent decision support systems for classification and prediction of the functional state of complex systems in the article. To predict the state of complex systems, hybrid decision modules with virtual flows are proposed, which reflect the hidden system connections between real and virtual data. The vector of informative features at the input of the hybrid decision module consists of two subsectors, the first of which corresponds to real flows, and the second - to virtual flows. Simulation modeling of classification processes using latent variables was performed, which allowed to evaluate the effect on the quality of classification of artificially introduced virtual flows. The structure of a neural network model with virtual recurrent-type streams is developed. The structure consists of N consecutively included neural network approximants. The outputs of the previous approximators are combined with the vector of in-formative attributes of the subsequent approximators, which allows forming virtual flows of different dimensions. A method is developed for the formation of non-linear models of virtual flows, characterized by the use of the GMDH-simulation method to obtain models of the influence of real flows on virtual flows, learned through nonlinear adalines. The method makes it possible to form a subvector of latent variables of unlimited dimension. Non-linear models of virtual flows are formed through a method based on the use of GMDH modeling. The method makes it possible to obtain neural network structures built on the basis of GMDH models and nonlinear adalines, which make it possible to form a subvector of latent variables of unlimited dimensionality.

scholarly journals Comparison of Non Linear Models and Artificial Neural Network to Describe the Liveweight from Birth to Maturity in Female Jersey Cattle

2021 ◽  
pp. 114-119
Author(s):  
D. O. Omoniwa ◽  
J. E. T. Akinsola ◽  
R. O. Okeke ◽  
J. M. Madu ◽  
D. S. Bunjah Umar
Keyword(s):  
Neural Network ◽  
Goodness Of Fit ◽  
Inflection Point ◽  
Linear Models ◽  
Network Models ◽  
Linear Functions ◽  
Growth Data ◽  
Neural Network Models ◽  
Jersey Cattle ◽  
Non Linear

Evaluation of growth data is an important strategy to manage gross feed requirement in female Jersey cattle in the New Derived Guinea Savannah Zone of Nigeria. Two non-linear functions (Gompertz and Logistic) and Neural network models were used to fit liveweight (LW)-age data using the non linear procedure of JMP statistical software. Data used for this study were collected from 150 Jersey female cattle in Shonga Dairy Farm, Kwara, State from 2010-2018. The Neural network function showedthe best goodness of fit. Both the Gompertz and Logistic functions overestimated LW at birth, 3, 36, 48, 60 and 72months respectively. NN function overestimated the LW at 0, 3, 24, 36 and 72 months. The Gompertzfunction had the best estimation of asymptotic weight (649.51 kg) with average absolute growth rate (0.061 kg/day).The inflection point was 15.95, 9.55 and 34.5 months in Logistic, Gompertz and neural network models, respectively. A strong and positive correlation was observed between asymptote and inflection point in Gompertz functions. The metrics of goodness of fit criteria (R2 and RMSE), showed that NN with multilayer perceptron was superior to the other models but Gompertz model, was best in its ability to approximate complex functions of growth curve parametersin female Jersey cattle.

scholarly journals Time Series Analysis and Forecasting of Rainfall for Agricultural Crops in India: An Application of Artificial Neural Network

2020 ◽  
Vol 12 (4) ◽  
pp. 1
Author(s):  
Debasis Mithiya ◽  
Kumarjit Mandal ◽  
Simanti Bandyopadhyay
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Time Series Analysis ◽  
Linear Model ◽  
Agricultural Production ◽  
Linear Models ◽  
Monthly Rainfall ◽  
Series Analysis ◽  
Non Linear

Indian agriculture depends heavily on rainfall. It not only influences agricultural production but also affects the prices of all agricultural commodities. Rainfall is an exogenous variable which is beyond farmers’ control. The outcome of rainfall fluctuation is quite natural. It has been observed that fluctuation in rainfall brings about fluctuation in output leading to price changes. Considering the importance of rainfall in determining agricultural production and prices, the study has attempted to forecast monthly rainfall in India with the help of time series analysis using monthly rainfall data. Both linear and non-linear models have been used. The value of diagnostic checking parameters (MAE, MSE, RMSE) is lower in a non-linear model compared to a linear one. The non-linear model - Artificial Neural Network (ANN) has been chosen instead of linear models, namely, simple seasonal exponential smoothing and Seasonal Auto-Regressive Integrated Moving Average to forecast rainfall. This will help to identify the proper cropping pattern.

Capability of an Elman Recurrent Neural Network for predicting the non-linear behavior of airborne pollutants

2021 ◽  
Author(s):  
David Barrero-González ◽  
Julio A. Ramírez-Montañez ◽  
Marco A. Aceves-Fernández ◽  
Juan M. Ramos-Arreguín
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Airborne Pollutants ◽  
Linear Behavior ◽  
Non Linear ◽  
Elman Recurrent Neural Network

Intelligent Numerical Manipulation of Micrometer-Scale Emulsions Using Polymer Confinement

2013 ◽  
Vol 813 ◽  
pp. 431-434 ◽  
Author(s):  
Li Guo Zhang ◽  
Le Xun Xue ◽  
Pei Yuan He ◽  
Yuan Ming Qi ◽  
Yu Min Lu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Material Science ◽  
Linear Models ◽  
Network Modeling ◽  
Neural Network Modeling ◽  
Linear Modeling ◽  
Artificial Neural Network Modeling ◽  
Non Linear ◽  
Micrometer Scale

The manipulation of emulsions at micrometer-scale is a challenging topic for industrial application, especially for monodisperse microemulsions production. The development of material science and afterwards the creation of polymer confinement proposed efficient devices for micrometer scale emulsions fabrication. In this work, the flow regime of emulsion generation was studied to depict numerical manipulation of micrometer-scale emulsions through biomicrofluidic technology. At first, correlation analysis between experiment conditions and results were conducted, then different linear modeling and non-linear modeling, including Artificial Neural Network Modeling (NNM) technology, were performed to characterize the emulsion variation. Both models can well manipulate emulsion variation. Compared with linear modeling, non-linear models ameliorate the performance on the manipulation of micrometer-scale emulsion.

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