CONSTRUCTIVE ESTIMATION OF APPROXIMATION FOR TRIGONOMETRIC NEURAL NETWORKS

2012 ◽  
Vol 10 (03) ◽  
pp. 1250021
Author(s):  
JIANJUN WANG ◽  
WEIHUA XU ◽  
BIN ZOU
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Lower Bound ◽  
Upper Bound ◽  
Intrinsic Property ◽  
Order Of Approximation ◽  
Integrable Functions ◽  
Hidden Layer ◽  
Hidden Neurons ◽  
Bound Estimation

For the three-layer artificial neural networks with trigonometric weights coefficients, the upper bound and lower bound of approximating 2π-periodic pth-order Lebesgue integrable functions [Formula: see text] are obtained in this paper. Theorems we obtained provide explicit equational representations of these approximating networks, the specification for their numbers of hidden-layer units, the lower bound estimation of approximation, and the essential order of approximation. The obtained results not only characterize the intrinsic property of approximation of neural networks, but also uncover the implicit relationship between the precision (speed) and the number of hidden neurons of neural networks.

Human Immunoglobulin G Adsorption in Epoxy Chitosan/Alginate Adsorbents: Evaluation of Isotherms by Artificial Neural Networks

2019 ◽  
Vol 14 (4) ◽  
Author(s):  
Ana Carolina Moreno Pássaro ◽  
Tainá Maia Mozetic ◽  
Jones Erni Schmitz ◽  
Ivanildo José da Silva ◽  
Tiago Dias Martins ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Adsorption Capacity ◽  
Protein Interactions ◽  
Nonlinear Models ◽  
Correlation Coefficients ◽  
Sodium Phosphate Buffer ◽  
Artificial Neural ◽  
Hidden Layer ◽  
Hidden Neurons

Abstract This work aimed to evaluate the interaction of human IgG in non-conventional adsorbents based on chitosan and alginate in the absence and presence of Reactive Green, Reactive Blue and Cibacron Blue immobilized as ligands. The adsorption was evaluated at 277, 288, 298 and 310 K using sodium phosphate buffer, pH 7.6, at 25 mmol L−1. The highest adsorption capacity was observed in the experiments performed with no immobilized dye, although all showed adsorption capacity higher than 120 mg g−1. Data modeling was done using Langmuir, Langmuir-Freundlich and Temkin classical nonlinear models, and artificial neural networks (ANN) for comparison. According to the parameters obtained, a possible adsorption in multilayers was observed due to protein-adsorbent and protein-protein interactions, concluding that IgG adsorption process is favorable and spontaneous. Using an ANN structure with 3 hidden neurons (single hidden layer), the MSE (RMSE) for training, test and validation were 13.698 (3.701), 11.206 (3.347) and 7.632 (2.763), respectively, achieving correlation coefficients of 0.999 in all steps. ANN modeling proved to be effective in predicting the adsorption isotherms in addition to overcoming the difficulties caused by experimental errors and/or arising from adsorption phenomenology.

scholarly journals Exploiting heterogeneity in operational neural networks by synaptic plasticity

2021 ◽  
Author(s):  
Serkan Kiranyaz ◽  
Junaid Malik ◽  
Habib Ben Abdallah ◽  
Turker Ince ◽  
Alexandros Iosifidis ◽  
...  
Keyword(s):  
Neural Networks ◽  
Synaptic Plasticity ◽  
Network Model ◽  
Neuron Model ◽  
Linear Operators ◽  
Training Data ◽  
Learning Performance ◽  
Minimal Network ◽  
Hidden Layer ◽  
Hidden Neurons

AbstractThe recently proposed network model, Operational Neural Networks (ONNs), can generalize the conventional Convolutional Neural Networks (CNNs) that are homogenous only with a linear neuron model. As a heterogenous network model, ONNs are based on a generalized neuron model that can encapsulate any set of non-linear operators to boost diversity and to learn highly complex and multi-modal functions or spaces with minimal network complexity and training data. However, the default search method to find optimal operators in ONNs, the so-called Greedy Iterative Search (GIS) method, usually takes several training sessions to find a single operator set per layer. This is not only computationally demanding, also the network heterogeneity is limited since the same set of operators will then be used for all neurons in each layer. To address this deficiency and exploit a superior level of heterogeneity, in this study the focus is drawn on searching the best-possible operator set(s) for the hidden neurons of the network based on the “Synaptic Plasticity” paradigm that poses the essential learning theory in biological neurons. During training, each operator set in the library can be evaluated by their synaptic plasticity level, ranked from the worst to the best, and an “elite” ONN can then be configured using the top-ranked operator sets found at each hidden layer. Experimental results over highly challenging problems demonstrate that the elite ONNs even with few neurons and layers can achieve a superior learning performance than GIS-based ONNs and as a result, the performance gap over the CNNs further widens.

Optimization Artificial Neural Network Using Artificial Bee Colony in Letter Recognition Classification

2020 ◽  
Vol 8 (4) ◽  
pp. 469
Author(s):  
I Gusti Ngurah Alit Indrawan ◽  
I Made Widiartha
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Classification Accuracy ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Letter Recognition ◽  
Bee Colony ◽  
Artificial Neural ◽  
Hidden Layer

Artificial Neural Networks or commonly abbreviated as ANN is one branch of science from the field of artificial intelligence which is often used to solve various problems in fields that involve grouping and pattern recognition. This research aims to classify Letter Recognition datasets using Artificial Neural Networks which are weighted optimally using the Artificial Bee Colony algorithm. The best classification accuracy results from this study were 92.85% using a combination of 4 hidden layers with each hidden layer containing 10 neurons.

Towards an Improved Ensemble Learning Model of Artificial Neural Networks

2016 ◽  
pp. 762-793
Author(s):  
Fatai Anifowose ◽  
Jane Labadin ◽  
Abdulazeez Abdulraheem
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Reservoir Characterization ◽  
Evaluation Criteria ◽  
Search Space ◽  
Combination Rules ◽  
Search Spaces ◽  
Ensemble Machine Learning ◽  
Artificial Neural ◽  
Hidden Neurons

Artificial Neural Networks (ANN) have been widely applied in petroleum reservoir characterization. Despite their wide use, they are very unstable in terms of performance. Ensemble machine learning is capable of improving the performance of such unstable techniques. One of the challenges of using ANN is choosing the appropriate number of hidden neurons. Previous studies have proposed ANN ensemble models with a maximum of 50 hidden neurons in the search space thereby leaving rooms for further improvement. This chapter presents extended versions of those studies with increased search spaces using a linear search and randomized assignment of the number of hidden neurons. Using standard model evaluation criteria and novel ensemble combination rules, the results of this study suggest that having a large number of “unbiased” randomized guesses of the number of hidden neurons beyond 50 performs better than very few occurrences of those that were optimally determined.

scholarly journals Prediction of Horizontal Daily Global Solar Irradiation Using Artificial Neural Networks (ANNs) in the Castile and León Region, Spain

Agronomy ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 96
Author(s):  
Francisco J. Diez ◽  
Luis M. Navas-Gracia ◽  
Leticia Chico-Santamarta ◽  
Adriana Correa-Guimaraes ◽  
Andrés Martínez-Rodríguez
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Predictive Modelling ◽  
Solar Irradiation ◽  
Data Series ◽  
Markov Analysis ◽  
Global Solar Irradiation ◽  
Ann Models ◽  
Artificial Neural ◽  
Hidden Layer

This article evaluates horizontal daily global solar irradiation predictive modelling using artificial neural networks (ANNs) for its application in agricultural sciences and technologies. An eight year data series (i.e., training networks period between 2004–2010, with 2011 as the validation year) was measured at an agrometeorological station located in Castile and León, Spain, owned by the irrigation advisory system SIAR. ANN models were designed and evaluated with different neuron numbers in the input and hidden layers. The only neuron used in the outlet layer was the global solar irradiation simulated the day after. Evaluated values of the input data were the horizontal daily global irradiation of the current day [H(t)] and two days before [H(t−1), H(t−2)], the day of the year [J(t)], and the daily clearness index [Kt(t)]. Validated results showed that best adjustment models are the ANN 7 model (RMSE = 3.76 MJ/(m2·d), with two inputs ([H(t), Kt(t)]) and four neurons in the hidden layer) and the ANN 4 model (RMSE = 3.75 MJ/(m2·d), with two inputs ([H(t), J(t)]) and two neurons in the hidden layer). Thus, the studied ANN models had better results compared to classic methods (CENSOLAR typical year, weighted moving mean, linear regression, Fourier and Markov analysis) and are practically easier as they need less input variables.

scholarly journals Application of Artificial Neural Networks to Assess the Mycological State of Bulk Stored Rapeseeds

Agriculture ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 567
Author(s):  
Jolanta Wawrzyniak
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Control Systems ◽  
Multilayer Perceptron ◽  
Activation Function ◽  
Fungal Population ◽  
Support Tool ◽  
Artificial Neural ◽  
Hidden Layer ◽  
And Storage

Artificial neural networks (ANNs) constitute a promising modeling approach that may be used in control systems for postharvest preservation and storage processes. The study investigated the ability of multilayer perceptron and radial-basis function ANNs to predict fungal population levels in bulk stored rapeseeds with various temperatures (T = 12–30 °C) and water activity in seeds (aw = 0.75–0.90). The neural network model input included aw, temperature, and time, whilst the fungal population level was the model output. During the model construction, networks with a different number of hidden layer neurons and different configurations of activation functions in neurons of the hidden and output layers were examined. The best architecture was the multilayer perceptron ANN, in which the hyperbolic tangent function acted as an activation function in the hidden layer neurons, while the linear function was the activation function in the output layer neuron. The developed structure exhibits high prediction accuracy and high generalization capability. The model provided in the research may be readily incorporated into control systems for postharvest rapeseed preservation and storage as a support tool, which based on easily measurable on-line parameters can estimate the risk of fungal development and thus mycotoxin accumulation.

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