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.

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 Development of estimation and forecasting method in intelligent decision support systems

2021 ◽  
Vol 2 (4 (110)) ◽  
pp. 38-47
Author(s):  
Іgor Romanenko ◽  
Andrii Golovanov ◽  
Vitalii Khoma ◽  
Andrii Shyshatskyi ◽  
Yevhen Demchenko ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Object State ◽  
Multidimensional Time Series ◽  
Intelligent Decision ◽  
Temporal Models ◽  
Intelligent Decision Support Systems ◽  
Artificial Neural ◽  
Intelligent Decision Support

The method of estimation and forecasting in intelligent decision support systems is developed. The essence of the proposed method is the ability to analyze the current state of the object under analysis and the possibility of short-term forecasting of the object state. The possibility of objective and complete analysis is achieved through the use of improved fuzzy temporal models of the object state, an improved procedure for forecasting the object state and an improved procedure for training evolving artificial neural networks. The concepts of a fuzzy cognitive model, in contrast to the known fuzzy cognitive models, are connected by subsets of fuzzy influence degrees, arranged in chronological order, taking into account the time lags of the corresponding components of the multidimensional time series. This method is based on fuzzy temporal models and evolving artificial neural networks. The peculiarity of this method is the ability to take into account the type of a priori uncertainty about the state of the analyzed object (full awareness of the object state, partial awareness of the object state and complete uncertainty about the object state). The ability to clarify information about the state of the monitored object is achieved through the use of an advanced training procedure. It consists in training the synaptic weights of the artificial neural network, the type and parameters of the membership function, as well as the architecture of individual elements and the architecture of the artificial neural network as a whole. The object state forecasting procedure allows conducting multidimensional analysis, consideration and indirect influence of all components of a multidimensional time series with different time shifts relative to each other under uncertainty.

scholarly journals Improvement of the method of estimation and forecasting of the state of the monitoring object in intelligent decision support systems

2021 ◽  
Vol 4 (3(112)) ◽  
pp. 43-55
Author(s):  
Areej Adnan Abed ◽  
Iurii Repilo ◽  
Ruslan Zhyvotovskyi ◽  
Andrii Shyshatskyi ◽  
Spartak Hohoniants ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
The State ◽  
Intelligent Decision ◽  
Intelligent Decision Support Systems ◽  
Artificial Neural ◽  
Intelligent Decision Support

In order to objectively and completely analyze the state of the monitored object with the required level of efficiency, the method for estimating and forecasting the state of the monitored object in intelligent decision support systems was improved. The essence of the method is to provide an analysis of the current state of the monitored object and short-term forecasting of the state of the monitored object. Objective and complete analysis is achieved using advanced fuzzy temporal models of the object state, taking into account the type of uncertainty and noise of initial data. The novelty of the method is the use of an improved procedure for processing initial data in conditions of uncertainty, an improved procedure for training artificial neural networks and an improved procedure for topological analysis of the structure of fuzzy cognitive models. The essence of the training procedure is the training of synaptic weights of the artificial neural network, the type and parameters of the membership function and the architecture of individual elements and the architecture of the artificial neural network as a whole. The procedure of forecasting the state of the monitored object allows for multidimensional analysis, accounting and indirect influence of all components of the multidimensional time series with their different time shifts relative to each other in conditions of uncertainty. The method allows increasing the efficiency of data processing at the level of 12–18 % using additional advanced procedures. The proposed method can be used in decision support systems of automated control systems (ACS DSS) for artillery units, special-purpose geographic information systems. It can also be used in ACS DSS for aviation and air defense and ACS DSS for logistics of the Armed Forces of Ukraine

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.

scholarly journals Development of object state estimation method in intelligent decision support systems

2021 ◽  
Vol 5 (3 (113)) ◽  
pp. 54-64
Author(s):  
Vitalii Bezuhlyi ◽  
Volodymyr Oliynyk ◽  
Іgor Romanenko ◽  
Oleksandr Zhuk ◽  
Vasyl Kuzavkov ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Decision Support ◽  
Initial Data ◽  
Object State ◽  
Intelligent Decision ◽  
Intelligent Decision Support Systems ◽  
Artificial Neural ◽  
Intelligent Decision Support

A method of object state estimation in intelligent decision support systems (DSS) has been developed. The essence of the method is to ensure a high-quality analysis of the current state of the analyzed object. The key difference of the developed method is the use of an advanced genetic algorithm. The advanced genetic algorithm is used when constructing a fuzzy cognitive model and increases the efficiency of identifying factors and relationships between them by simultaneously finding a solution by several individuals. The objective and complete analysis is achieved using advanced fuzzy temporal models of the object state, taking into account the type of uncertainty and noise of initial data. The method also contains an improved procedure for processing initial data under a priori uncertainty, an improved procedure for training artificial neural networks and an improved procedure for topological analysis of the structure of fuzzy cognitive models. The essence of the training procedure is the training of synaptic weights of the artificial neural network, the type and parameters of the membership function, as well as the architecture of individual elements and the architecture of the artificial neural network as a whole. The method increases the efficiency of data processing at the level of 11–15 % using additional advanced procedures. The proposed method can be used in DSS of automated control systems (artillery units, special-purpose geographic information systems). It can also be used in DSS for aviation and air defense ACS, as well as in DSS for logistics ACS of the Armed Forces

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.

scholarly journals Are Neural Network Models Truly Effective at Forecasting? An Evaluation of Forecast Performance of Traditional Models with Neural Network Model for the Macroeconomic Data of G-7 Countries

2020 ◽  
Vol 11 (3) ◽  
pp. 49-52
Author(s):  
Tayyab Raza Fraz ◽  
Samreen Fatima
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Linear Models ◽  
Arima Models ◽  
Neural Network Models ◽  
Economic Variables ◽  
Forecast Performance ◽  
Non Linear ◽  
Auto Regressive

Forecasting macroeconomic and financial data are always difficult task to the researchers. Various statisticaland econometrics techniques have been used to forecast these variables more accurately. Furthermore, in the presenceof structural break, linear models are failed to model and forecast. Therefore, this study examines the forecastingperformance of economic variables of G7 countries: France, Italy, Canada, Germany, Japan, United Kingdom andUnited States of America using non-linear autoregressive neural network (ARNN) model, linear auto regressive (AR)and Auto regressive integrated moving average model (ARIMA) models. The economic variables are inflation,exchange rate and Gross Domestic Product (GDP) growth for the period from 1970 to 2015. To measure theperformance of the considered model Root, Mean Square Error, Mean Absolute Error and Mean Absolute PercentageError are used. The results show that the forecasts from the non-linear neural network model are undoubtedly better ascompared to the AR and the Box–Jenkins ARIMA models.

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