generalized regression neural networks
Modeling of Resilient Modulus of Asphalt Concrete Containing Reclaimed Asphalt Pavement Using Feed-Forward and Generalized Regression Neural Networks
... artificial neural network (ANN) models for modeling and evaluating the effects of different percentages of RAP on resilient modulus of hot-mix asphalt ...feed-forward neural network (FFNN) and ...
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Deriving amplification factors from simple site parameters using generalized regression neural networks: implications for relevant site proxies
... context, the linear, viscoelastic responses of a set of 858 actual soil columns from Japan, the USA, and Europe are com‑ puted for a set of 14 real accelerograms with varying frequency contents. The correlation between ...
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Comparison of Artificial Intelligence Techniques for river flow forecasting
... Artificial Neural Network (ANN) methods, Generalized Regression Neural Networks (GRNN) and Feed Forward Neural Networks (FFNN), and Auto-Regressive (AR) models for ...
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II. DEVELOPING A NEW ALGORITHM
... Abstract—We propose a simple approach for forecasting univariate time series. The proposed algorithm is an ensemble learning technique that combines the advice from several Generalized Regression ...
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Application of Artificial Neural Network and Adaptive Neural based Fuzzy Inference System Techniques in Estimating of Virtual Water
... and Generalized Regression Neural Networks (GRNN) as well as ANFIS were examined in estimation of VW using measured data of the yield and Crop Water Requirement (CWR) of common crops, and ...
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Fingerprint Classification Using Kernel Smoothing Technique and Generalized Regression Neural Network and Probabilistic Neural Network
... The second layer of PNN is a competitive layer in comparison with GRNN which is a linear layer. Two hundred images in the database are used in the training phase. Each image includes twenty two figures. Plus, the spread ...
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Generalized Regression Neural Network Based on Soft Sensor for Multicomponent Distillation Column
... The attempt to conjugate GA with ANN by the evolution of connection weights is discussed in our earlier paper [5]. The main aspect of neural networks design is the selection of training methods and ...
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The Research of Tax Inspection Based on Generalized Regression Neural Network
... contrast, neural networks can com- plete the non-linear modeling on condition of not understanding the relationship of the input or output vari- ...and neural networks have the characteristics ...
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A Unique Approach to Epilepsy Classification from EEG Signals Using Dimensionality Reduction and Neural Networks
... the Neural Networks (NN) such as Cascaded Feed Forward Neural Network (CFFNN), Time Delay Neural Network (TDNN) and Generalized Regression Neural Network (GRNN) are used ...
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Machine learning methods for empirical streamflow simulation: a comparison of model accuracy, interpretability, and uncertainty in seasonal watersheds
... artificial neural networks, despite the de- velopment of multiple other approaches for non-parametric regression in recent ...multiple regression and machine learning approaches (including ...
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Evaluation of food logistics system based on generalized regression neural network
... [2]. Generalized Regression Neural Network (GRNN), is a kind of neural networks ...applies generalized regression neural network to evaluate and analyze food ...
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A Radial Basis Function Approach to Retrieve Soil Moisture and Crop Variables from X-Band Scatterometer Observations
... function neural networks (RBFNN) have proved to be very good function approximators as well as classifiers for a wide variety of remotely sensed ...multilayer neural networks ...
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Interpolation of Generalized Functions Using Artificial Neural Networks
... of generalized functions or distributions. The theory of generalized functions is developed by Sergei So- bolev (1930s) and Laurent Schwartz (1940s) (for details see the monographs [1] [2] [3] and the ...
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PREDICTION OF PERFORMANCE AND EMISSIONS OFCI ENGINE FUELLED WITH SUNFLOWER OIL AND ITS BLENDS WITH DIESEL USING ARTIFICIAL NEURAL NETWORK
... artificial neural networks (ANN) are used to create such computationally efficient ...propagation neural network is developed with number of nodes in hidden layers and training rule are varied to ...
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An Overview of Classification Algorithms and Ensemble Methods in Personal Credit Scoring
... Logistic regression, work is same like discriminant analysis, is also one of the most widely used statistical techniques in the field of data ...Logistic regression is the generalization of linear ...linear ...
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Prediction of Electrical Output Power of Combined Cycle Power Plant Using Regression ANN Model
... Artificial neural networks (ANNs) or connectionist systems are a computational model used in computer science and other research disciplines, which is based on a large collection of simple neural ...
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5633.pdf
... artificial neural networks (ANNs), regression, and rainfall-based models are currently being used, but multiple linear regression (MLR) models are the most commonly employed in different types ...
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Comparative Analysis of Regression based and Supervised Learning Algorithms for Predicting Traffic Noise Levels in Indian Scenario
... Here, in our work we require to use the multi linear regression results because there can be more than one factor on which the noise produced by the road traffic can be depended. In our cases the volume of ...
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... like regression analysis, multivariate analysis and pattern recognition models have been applied to a wide range of decisions in many ...methodology. Neural networks share the advantages with the ...
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Analysis of Cost Overrun in Highway Construction Projects using Multiple Regression and Artificial Neural Networks
... Elbeltagi et.al [3] investigated factors that significantly influence highway construction and an artificial neural network model is developed for predicting the cost. The network is trained and tested with a ...
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