[PDF] Top 20 Fuzzy nonlinear regression using artificial neural networks

... Abstract: Fuzzy linear regression analysis with symmetric triangular fuzzy number coefficient has been introduced by Tanaka et ...the fuzzy nonlinear regression using ... See full document

... The artificial neural network (ANN), an artificial intelligence technique, is a potential tool for modeling data in poultry ...of using ANN methodology to estimate production parameters of ... See full document

... an artificial NN approach for nonlinear modelling of multivariate streamflow ...series using multi-layer feed- forward error-backpropagation NN, with iterated multi-step prediction, where the single ... See full document

... techniques using the same input data, and the results illustrated the importance of terrain roughness and soil type as key factors within the model; using only these two variables, the analysis returned a ... See full document

... Advangeo® provides the software environment for effective data pre-processing, step-by-step model generation, and result visualisation. It is a tool to build up structured and comprehensible models within the widely used ... See full document

... Once the ensemble is trained, the topology of the gene regulatory network is obtained by applying a second procedure. Considering each gene in the network separately, we pass a value of 1 to the input neuron of the ... See full document

... highly nonlinear relation between the compressive strength of masonry and the geometrical and mechanical properties of the components of the ...ficial neural networks for predicting the compressive ... See full document

... Jordan using Artificial Neural Networks (ANNs) ...Generalized Regression Neural Network (GRNN) is used to predict the diseases ...largest regression value affect the acute ... See full document

... a fuzzy neural network-based in-process mixed material- caused flash prediction (FNN-IPMFP) system for injection molding ...a fuzzy neural network to predict flash in injection molding ... See full document

... Now using fuzzy regression which is clarified before, the price of electricity is ...perform fuzzy regression the input variables should be indicated, here the input variables are the ... See full document

... extraction using a minicolumn packed with Amberlite XAD-4 modified with 3,4-dihydroxybenzoic ...optimized using Doehlert ...squares regression and artificial neural networks) ... See full document

... Artificial neural networks (ANNs), as one of the most attractive branches in artificial intelligence, has the potential to handle problems such as modeling, estimating, prediction, ... See full document

... Artificial Neural Networks (ANN) form a classification technique where neurons are trained to detect patterns in the training dataset and then the trained classifier is applied to unknown ...and ... See full document

... In this study the most common neural network type, the multilayer perceptron, was adopted. This type of network is formed by three or more layers of basic computing units named artificial neurons or nodes. ... See full document

... An ANN is a system inspired by the operation of biological neurons with the purpose of learning a certain system. The construction of an ANN is achieved by providing a stimu- lus to the neuronal model, calculating the ... See full document

... in artificial and fuzzy neural networks technique has been conducted to separate classes of forest from non-forest using SPOT satellite neural network method with the accuracy of ... See full document

... Coordinate transformation is necessary in the surveying and mapping industry particularly in developing countries like Ghana where the non-geocentric datum which is still utilized is highly heterogeneous. It also creates ... See full document

... stabilization fuzzy controller of the spherical mobile robot is ...five fuzzy sets for each input variable then there are 625 fuzzy rules in this fuzzy ...the fuzzy implication but also ... See full document

... appropriate neural network, able to simulate the behavior of proximity sensor at different functioning conditions, we choose a high degree of parameterization for the application, with different kind of ... See full document

... [r] ... See full document