scholarly journals Perceptron Architecture Ensuring Pattern Description Compactness

2009 ◽  
Vol 40 (1) ◽  
pp. 87-93
Author(s):  
Sergejs Jakovlevs
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Linear Transformation ◽  
Multilayer Perceptron ◽  
Dimensional Space ◽  
Linear Case ◽  
Initial Space ◽  
Higher Dimensional ◽  
Non Linear ◽  
Pattern Description

Perceptron Architecture Ensuring Pattern Description CompactnessThis paper examines conditions a neural network has to meet in order to ensure the formation of a space of features satisfying the compactness hypothesis. The formulation of compactness hypothesis is defined in more detail as applied to neural networks. It is shown that despite the fact that the first layer of connections is formed randomly, the presence of more than 30 elements in the middle network layer guarantees a 100% probability that the G-matrix of the perceptron will not be special. It means that under additional mathematical calculations made by Rosenblatt, the perceptron will with guaranty form a space of features that could be then linearly divided. Indeed, Cover's theorem only says that separation probability increases when the initial space is transformed into a higher dimensional space in the non-linear case. It however does not point when this probability is 100%. In the Rosenblatt's perceptron, the non-linear transformation is carried out in the first layer which is generated randomly. The paper provides practical conditions under which the probability is very close to 100%. For comparison, in the Rumelhart's multilayer perceptron this kind of analysis is not performed.

scholarly journals Prediction of the chrome distribution in subarctic Noyabrsk using co-kriging, generalized regression neural network, multilayer perceptron, and hybrid technics

2019 ◽  
pp. 77-86
Author(s):  
A. G. Buevich ◽  
I. E. Subbotina ◽  
A. V. Shichkin ◽  
A. P. Sergeev ◽  
E. M. Baglaeva
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Multilayer Perceptron ◽  
Surface Soil ◽  
Soil Layer ◽  
Generalized Regression Neural Network ◽  
Residual Kriging ◽  
Surface Soil Layer ◽  
Artificial Neural ◽  
Generalized Regression

Combination of geostatistical interpolation (kriging) and machine learning (artificial neural networks, ANN) methods leads to an increase in the accuracy of forecasting. The paper considers the application of residual kriging of an artificial neural network to predicting the spatial contamination of the surface soil layer with chromium (Cr). We reviewed and compared two neural networks: the generalized regression neural network (GRNN) and multilayer perceptron (MLP), as well as the combined method: multilayer perceptron residual kriging (MLPRK). The study is based on the results of the screening of the surface soil layer in the subarctic Noyabrsk, Russia. The models are developed based on computer modeling with minimization of the RMSE. The MLPRK model showed the best prognostic accuracy.

MOVEMENT AND MEMORY FUNCTION IN BIOLOGICAL NEURAL NETWORKS

1995 ◽  
Vol 04 (04) ◽  
pp. 489-500 ◽  
Author(s):  
NAOHIRO ISHII ◽  
KEN-ICHI NAKA
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Spatial Information ◽  
Memory Function ◽  
Amacrine Cells ◽  
Linear Analysis ◽  
Non Linear ◽  
Asymmetric Networks ◽  
Direction Of Movement ◽  
The Difference

Asymmetrical neural networks are shown in the biological neural network as the catfish retina. Horizontal and bipolar cell responses are linearly related to the input modulation of light, while amacrine cells work linearly and nonlinearly in their responses. These cells make asymmetrical neural networks in the retina. Several mechanisms have been proposed for the detection of motion in biological system. To make clear the difference among asymmetrical networks, we applied non-linear analysis developed by N. Wiener. Then, we can derive α-equation of movement, which shows the direction of movement. During the movement, we also can derive the movement equation, which implies that the movement holds regardless of the parameter α. By analyzing the biological asymmetric neural networks, it is shown that the asymmetric networks are excellent in the ability of spatial information processing on the retinal level. Then, the symmetric network was discussed by applying the non-linear analysis. In the symmetric neural network, it was suggested that memory function is needed to perceive the movement.

Modelling and control of greenhouse system using neural networks

2016 ◽  
Vol 40 (3) ◽  
pp. 918-929 ◽  
Author(s):  
A Manonmani ◽  
T Thyagarajan ◽  
M Elango ◽  
S Sutha
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Moving Average ◽  
Growth Conditions ◽  
Operating Conditions ◽  
Adverse Weather ◽  
Control Schemes ◽  
Non Linear ◽  
Auto Regressive ◽  
And Control

A greenhouse system (GHS) is a closed structure that facilitates modified growth conditions to crops and provides protection from pests, diseases and adverse weather. However, a GHS exhibits non-linearity due to the interaction between the biological subsystem and the physical subsystem. Non-linear systems are difficult to control, particularly when their characteristics change with time. These systems are best handled with methods of computation intelligence, such as artificial neural networks (ANNs) and fuzzy systems. In the present work, the approximation capability of a neural network is used to model and control sufficient growth conditions of a GHS. An optimal neural network-based non-linear auto regressive with exogenous input (NARX) time series model is developed for a GHS. Based on the NARX model, two intelligent control schemes, namely a neural predictive controller (NPC) and non-linear auto regressive moving average (NARMA-L2) controller are proposed to achieve the desired growth conditions such as humidity and temperature for a better yield. Finally, closed-loop performances of the above two control schemes for servo and regulatory operations are analysed for various operating conditions using performance indices.

scholarly journals Use of a Multilayer Perceptron to Automate Terrain Assessment for the Needs of the Armed Forces

2018 ◽  
Vol 7 (11) ◽  
pp. 430 ◽  
Author(s):  
Krzysztof Pokonieczny
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Multilayer Perceptron ◽  
Learning Algorithm ◽  
Armed Forces ◽  
Shuttle Radar Topography Mission ◽  
Terrain Classification ◽  
The Neural Network ◽  
Data Configuration ◽  
Trained Neural Network

The classification of terrain in terms of passability plays a significant role in the process of military terrain assessment. It involves classifying selected terrain to specific classes (GO, SLOW-GO, NO-GO). In this article, the problem of terrain classification to the respective category of passability was solved by applying artificial neural networks (multilayer perceptron) to generate a continuous Index of Passability (IOP). The neural networks defined this factor for primary fields in two sizes (1000 × 1000 m and 100 × 100 m) based on the land cover elements obtained from Vector Smart Map (VMap) Level 2 and Shuttle Radar Topography Mission (SRTM). The work used a feedforward neural network consisting of three layers. The paper presents a comprehensive analysis of the reliability of the neural network parameters, taking into account the number of neurons, learning algorithm, activation functions and input data configuration. The studies and tests carried out have shown that a well-trained neural network can automate the process of terrain classification in terms of passability conditions.

scholarly journals PD Control Compensation Based on a Cascade Neural Network Applied to a Robot Manipulator

2020 ◽  
Vol 14 ◽  
Author(s):  
Luis Arturo Soriano ◽  
Erik Zamora ◽  
J. M. Vazquez-Nicolas ◽  
Gerardo Hernández ◽  
José Antonio Barraza Madrigal ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Robot Manipulator ◽  
Industrial Robot ◽  
Identification Problem ◽  
Adaptive Controller ◽  
Pd Control ◽  
Non Linear ◽  
Cascade Neural Network ◽  
Integral Gain

A Proportional Integral Derivative (PID) controller is commonly used to carry out tasks like position tracking in the industrial robot manipulator controller; however, over time, the PID integral gain generates degradation within the controller, which then produces reduced stability and bandwidth. A proportional derivative (PD) controller has been proposed to deal with the increase in integral gain but is limited if gravity is not compensated for. In practice, the dynamic system non-linearities frequently are unknown or hard to obtain. Adaptive controllers are online schemes that are used to deal with systems that present non-linear and uncertainties dynamics. Adaptive controller use measured data of system trajectory in order to learn and compensate the uncertainties and external disturbances. However, these techniques can adopt more efficient learning methods in order to improve their performance. In this work, a nominal control law is used to achieve a sub-optimal performance, and a scheme based on a cascade neural network is implemented to act as a non-linear compensation whose task is to improve upon the performance of the nominal controller. The main contributions of this work are neural compensation based on a cascade neural networks and the function to update the weights of neural network used. The algorithm is implemented using radial basis function neural networks and a recompense function that leads longer traces for an identification problem. A two-degree-of-freedom robot manipulator is proposed to validate the proposed scheme and compare it with conventional PD control compensation.

Elevating Model Predictive Control Using Feedforward Artificial Neural Networks: A Review

2009 ◽  
Vol 4 (1) ◽  
Author(s):  
Senthil Kumar Arumugasamy ◽  
Zainal Ahmad
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Model Based Control ◽  
Model Based ◽  
Non Linear ◽  
Artificial Neural ◽  
Neural Predictive Control

Process control in the field of chemical engineering has always been a challenging task for the chemical engineers. Hence, the majority of processes found in the chemical industries are non-linear and in these cases the performance of the linear models can be inadequate. Recently a promising alternative modelling technique, artificial neural networks (ANNs), has found numerous applications in representing non-linear functional relationships between variables. A feedforward multi-layered neural network is a highly connected set of elementary non-linear neurons. Model-based control techniques were developed to obtain tighter control. Many model-based control schemes have been proposed to incorporate a process model into a control system. Among them, model predictive control (MPC) is the most common scheme. MPC is a general and mathematically feasible scheme to integrate our knowledge about a target, process controller design and operation, which allows flexible and efficient exploitation of our understanding of a target, and thus produces optimal performance of a system under various constraints. The need to handle some difficult control problems has led us to use ANN in MPC and has recently attracted a great deal of attention. The efficacy of the neural predictive control with the ability to perform comparably to the non linear neural network strategy in both set point tracking and disturbance rejection proves to have less computation expense for the neural predictive control. The neural network model predictive control (NNMPC) method has less perturbations and oscillations when dealing with noise as compared to the PI controllers.

Estimating the Uncertainty of a Multilayer Perceptron Using the Monte Carlo Method

2012 ◽  
Vol 628 ◽  
pp. 324-329
Author(s):  
F. García Fernández ◽  
L. García Esteban ◽  
P. de Palacios ◽  
A. García-Iruela ◽  
R. Cabedo Gallén
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Multilayer Perceptron ◽  
Good Accuracy ◽  
Output Uncertainty ◽  
The Monte Carlo Method ◽  
Artificial Neural

Artificial neural networks have become a powerful modeling tool. However, although they obtain an output with very good accuracy, they provide no information about the uncertainty of the network or its coverage intervals. This study describes the application of the Monte Carlo method to obtain the output uncertainty and coverage intervals of a particular type of artificial neural network: the multilayer perceptron.

scholarly journals Classification of heterogeneous precipitation fields for the assessment and possible improvement of lumped neural network models for streamflow forecasts

2006 ◽  
Vol 3 (1) ◽  
pp. 201-227 ◽  
Author(s):  
N. Lauzon ◽  
F. Anctil ◽  
C. W. Baxter
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Multilayer Perceptron ◽  
Network Models ◽  
Rainfall Runoff ◽  
Neural Network Models ◽  
Kohonen Neural Network ◽  
Streamflow Forecasts ◽  
Rainfall Runoff Model

Abstract. This work addresses the issue of better considering the heterogeneity of precipitation fields within lumped rainfall-runoff models where only areal mean precipitation is usually used as an input. A method using a Kohonen neural network is proposed for the classification of precipitation fields. The evaluation and improvement of the performance of a lumped rainfall-runoff model for one-day ahead predictions is then established based on this classification. Multilayer perceptron neural networks are employed as lumped rainfall-runoff models. The Bas-en-Basset watershed in France, which is equipped with 23 rain gauges with data for a 21-year period, is employed as the application case. The results demonstrate the relevance of the proposed classification method, which produces groups of precipitation fields that are in agreement with the global climatological features affecting the region, as well as with the topographic constraints of the watershed (i.e., orography). The strengths and weaknesses of the rainfall-runoff models are highlighted by the analysis of their performance vis-à-vis the classification of precipitation fields. The results also show the capability of multilayer perceptron neural networks to account for the heterogeneity of precipitation, even when built as lumped rainfall-runoff models.

scholarly journals Disparities in particulate matter (PM<sub>10</sub>) origins and oxidative potential at a city-scale (Grenoble, France) – Part II: Sources of PM<sub>10</sub> oxidative potential using multiple linear regression analysis and the predictive applicability of multilayer perceptron neural network analysis

2021 ◽  
Author(s):  
Lucille Joanna S. Borlaza ◽  
Samuël Weber ◽  
Jean-Luc Jaffrezo ◽  
Stephan Houdier ◽  
Rémy Slama ◽  
...  
Keyword(s):  
Neural Network ◽  
Particulate Matter ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Multilayer Perceptron ◽  
Mass Concentration ◽  
Redox Activity ◽  
Oxidative Potential ◽  
Atmospheric Processes ◽  
Non Linear

Abstract. The oxidative potential (OP) of particulate matter (PM) quantifies PM capability to cause anti-oxidant imbalance. Due to the wide range and complex mixture of species in particulates, little is known on the pollution sources most strongly contributing to OP. A one-year sampling of PM10 (particles with an aerodynamic diameter below 10) was performed over different sites in a medium-sized city (Grenoble, France). An enhanced fine-scale apportionment of PM10 sources, based on the chemical composition, was performed using Positive Matrix Factorization (PMF) method and reported in a companion paper (Borlaza et al., 2020). OP was assessed as the ability of PM10 to generate reactive oxygen species (ROS) using three different acellular assays: Dithiothreitol (DTT), Ascorbic acid (AA), and 2,7-dichlorofluorescein (DCFH) assays. Using multiple linear regression (MLR), the OP contribution of the sources identified by PMF were estimated. Conversely, since atmospheric processes are usually non-linear in nature, artificial neural network (ANN) techniques, which employs non-linear models, could further improve estimates. Hence, the multilayer perceptron analysis (MLP), an ANN-based model, was additionally used to model OP based on PMF-resolved sources as well. This study presents the spatiotemporal variabilities of OP activity with influences by season-specific sources, site typology and specific local features, and assay sensitivity. Overall, both MLR and MLP effectively captured the evolution of OP. The primary traffic and biomass burning sources were the strongest drivers of OP in the Grenoble basin. There is also a clear redistribution of source-specific impacts when using OP instead of mass concentration, underlining the importance of PM redox activity over mass concentration. Finally, the MLP generally offered improvements in OP prediction especially for sites where synergistic and/or antagonistic effects between sources are prominent, supporting the value of using ANN-based models to account for the non-linear dynamics behind the atmospheric processes affecting OP of PM10.

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