New directions in model development for RF/microwave components utilizing artificial neural networks and space mapping

2003 ◽  
Author(s):  
J.W. Bandler ◽  
M.A. Ismail ◽  
J.E. Rayas-Sanchez ◽  
Q.J. Zhang
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Model Development ◽  
Space Mapping ◽  
Microwave Components ◽  
New Directions ◽  
Artificial Neural ◽  
Rf Microwave

scholarly journals The evaluation on artificial neural networks (ANN) and multiple linear regressions (MLR) models over particulate matter (PM10) variability during haze and non-haze episodes: A decade case study

2019 ◽  
Vol 15 (2) ◽  
pp. 164-172 ◽  
Author(s):  
Ku Mohd Kalkausar Ku Yusof ◽  
Azman Azid ◽  
Muhamad Shirwan Abdullah Sani ◽  
Mohd Saiful Samsudin ◽  
Siti Noor Syuhada Muhammad Amin ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Particulate Matter ◽  
Meteorological Factors ◽  
Model Development ◽  
Eastern Region ◽  
Multiple Linear Regressions ◽  
Artificial Neural ◽  
Linear Regressions

The comprehensives of particulate matter studies are needed in predicting future haze occurrences in Malaysia. This paper presents the application of Artificial Neural Networks (ANN) and Multiple Linear Regressions (MLR) coupled with sensitivity analysis (SA) in order to recognize the pollutant relationship status over particulate matter (PM10) in eastern region. Eight monitoring studies were used, involving 14 input parameters as independent variables including meteorological factors. In order to investigate the efficiency of ANN and MLR performance, two different weather circumstances were selected; haze and non-haze. The performance evaluation was characterized into two steps. Firstly, two models were developed based on ANN and MLR which denoted as full model, with all parameters (14 variables) were used as the input. SA was used as additional feature to rank the most contributed parameter to PM10 variations in both situations. Next, the model development was evaluated based on selected model, where only significant variables were selected as input. Three mathematical indices were introduced (R2, RMSE and SSE) to compare on both techniques. From the findings, ANN performed better in full and selected model, with both models were completely showed a significant result during hazy and non-hazy. On top of that, UVb and carbon monoxide were both variables that mutually predicted by ANN and MLR during hazy and non-hazy days, respectively. The precise predictions were required in helping any related agency to emphasize on pollutant that essentially contributed to PM10 variations, especially during haze period.

Using Artificial Neural Networks in the Reactor Safety Research

2008 ◽  
Author(s):  
Alexander Kratzsch ◽  
Wolfgang Ka¨stner ◽  
Rainer Hampel
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Heat Dissipation ◽  
Model Development ◽  
Reactor Core ◽  
Differential Pressure ◽  
Reactor Safety ◽  
Safety Research ◽  
Artificial Neural ◽  
Residual Heat

The paper deals with the creation of a differential pressure model with artificial neural networks (ANN). Particular, model development and verification tests are considered. One of the main features in reactor safety research is the safe heat dissipation from the reactor core and the reactor containment of light-water reactors. In the case of loss of coolant accident (LOCA) the possibility of the entry of isolation material into the reactor containment and the building sump of the reactor containment and into the associated systems to the residual heat exhaust is a serious problem. This can lead to a handicap of the system functions. To ensure the residual heat exhaust it is necessary the emergency cooling systems to put in operation which transport the water from the sump to the condensation chamber and directly to the reactor pressure vessel. A high allocation of the sieves with fractionated isolation material, in the sump can lead to a blockage of the strainers, inadmissibly increase of differential pressure, build-up at the sieves and to malfunctioning pumps. Hence, the scaling and retention of fractionated isolation material in the building sump of the reactor containment must be estimated. This allows the potential plant status in case of incidents to be assessed. The differential pressure is the essential parameter for the assessment of allocation of the strainers. For modelling we use artificial neural networks. To build up the ANN, the available experimental data are used to train the ANN.

scholarly journals Development and Application of Back-Propagation-Based Artificial Neural Network Models in Solving Engineering

2002 ◽  
Vol 7 (1) ◽  
pp. 55
Author(s):  
Saleh Mohammed Al-Alawi
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Model Development ◽  
Back Propagation ◽  
Network Models ◽  
Research Area ◽  
Ann Model ◽  
Neural Network Models ◽  
Fields Of Study ◽  
Artificial Neural

Artificial Neural Networks (ANNs) are computer software programs that mimic the human brain's ability to classify patterns or to make forecasts or decisions based on past experience.  The development of this research area can be attributed to two factors, sufficient computer power to begin practical ANN-based research in the late 1970s and the development of back-propagation in 1986 that enabled ANN models to solve everyday business, scientific, and industrial problems.  Since then, significant applications have been implemented in several fields of study, and many useful intelligent applications and systems have been developed.  The objective of this paper is to generate awareness and to encourage applications development using artificial intelligence-based systems.  Therefore, this paper provides basic ANN concepts, outlines steps used for ANN model development, and lists examples of engineering applications based on the use of the back-propagation paradigm conducted in Oman.  The paper is intended to provide guidelines and necessary references and resources for novice individuals interested in conducting research in engineering or other fields of study using back-propagation artificial neural networks.      

scholarly journals Pronóstico de caudales medios mensuales del rio caplina, aplicando redes neuronales artificiales (rna) y modelo autorregresivo periódico de primer orden par (1)

2019 ◽  
pp. 69-72
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Stochastic Models ◽  
Autoregressive Model ◽  
Model Development ◽  
Water Current ◽  
Long Time ◽  
Artificial Neural ◽  
Monthly Discharge ◽  
Del Rio

Pronóstico de caudales medios mensuales del rio caplina, aplicando redes neuronales artificiales (rna) y modelo autorregresivo periódico de primer orden par (1) Forecast for mean monthly discharge of the caplina river, by applying artificial neural network (rna) and periodic Autoregressive model par (1) Pino Vargas Edwin, Siña Espinoza Luis, Román Arce Carmen Programa de Doctorado en Recursos Hídricos / U.N.Agraria La Molina, Lima Perú, [email protected] Universidad Nacional Jorge Basadre G. Tacna, [email protected] Universidad Nacional Jorge Basadre G. Tacna, [email protected] DOI: https://doi.org/10.33017/RevECIPeru2011.0025/ RESUMEN El rio Caplina es el principal tributario de la cuenca hidrográfica del mismo nombre; tiene una extensión de 4 239,09 km2, esto hace que sea una de las principales fuentes de abastecimiento de agua para distintos usos en la ciudad de Tacna. Por esta razón diversas entidades se han interesado en conocer la disponibilidad hídrica actual y futura del rio Caplina, ya que conocer dichos valores es de fundamental importancia para el planeamiento y manejo de los sistemas de recursos hídricos. Los modelos estocásticos han sido durante largo tiempo, la alternativa más común en la predicción de caudales. Actualmente, las herramientas de computación inteligente como las redes neuronales artificiales, especialmente las redes multi-capas con algoritmo de retro-propagación. En este contexto, la actual investigación centro sus esfuerzos en la aplicación de las redes neuronales a la predicción de los caudales medios mensuales del río Caplina-Estación Bocatoma Calientes, desarrollo de modelos de redes neuronales a partir de datos de caudales, precipitación y evaporación, así como la evaluación de la capacidad de desempeño frente a modelos estocásticos. De esta manera, se desarrollaron 10 modelos de redes neuronales artificiales con distintas arquitecturas, cuyo entrenamiento se realizo con un primer subconjunto de datos correspondientes al periodo 1939 – 1999, y su validación con un segundo subconjunto de datos del periodo 2000 – 2006. Los modelos de redes neuronales artificiales mostraron comparativamente mejor desempeño en materia de predicción frente a un modelo autorregresivo periódico de primer orden PAR (1). Descriptores: Cuenca Caplina, Redes Neuronales Artificiales, Series de Tiempo. ABSTRACT Caplina river is the main tributary of the hydrographic basin of the same name, It has an extension of 4 239,09 km2, because of this reason it is one of the principal sources of water supply for different uses in Tacna's city. For this reason diverse entities have been interested in knowing the water current and future availability of the river Caplina, because know the above mentioned values performs is the fundamental importance for the planning and managing of the systems of water resources. The stochastic models have been during long time, the most common alternative in the prediction of flows. Nowadays, the tools of intelligent computation like the artificial neural networks, specially the networks you multi-geld with algorithm of retro-spread. In this context, the current investigation center his efforts on the application of the neural networks to the prediction of the average monthly flows of the river Caplina-station Bocatoma Calientes, model development of neural networks from information of flows, rainfall and evaporation, as well as the evaluation of the capacity of performance opposite to stochastic models. So, 10 models of artificial neural networks were developed with different architectures, which training was realize with the first subset of information corresponding to the period 1939 - 1999, and his validation with the second subset of information of the period 2000 - 2006. The models of artificial neural networks showed comparatively better performance as for prediction opposite to a periodic autoregressive model of the first order PAR (1). Keywords: Caplina Basin, artificial neural networks, Series of Time.

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