scholarly journals Comparing the Selected Transfer Functions and Local Optimization Methods for Neural Network Flood Runoff Forecast

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Petr Maca ◽  
Pavel Pech ◽  
Jiri Pavlasek
Keyword(s):  
Neural Network ◽  
Transfer Functions ◽  
Optimization Methods ◽  
Local Optimization ◽  
Multilayer Perceptrons ◽  
Training Algorithms ◽  
Activation Functions ◽  
Marquardt Algorithm ◽  
Runoff Forecast ◽  
Hidden Layer

The presented paper aims to analyze the influence of the selection of transfer function and training algorithms on neural network flood runoff forecast. Nine of the most significant flood events, caused by the extreme rainfall, were selected from 10 years of measurement on small headwater catchment in the Czech Republic, and flood runoff forecast was investigated using the extensive set of multilayer perceptrons with one hidden layer of neurons. The analyzed artificial neural network models with 11 different activation functions in hidden layer were trained using 7 local optimization algorithms. The results show that the Levenberg-Marquardt algorithm was superior compared to the remaining tested local optimization methods. When comparing the 11 nonlinear transfer functions, used in hidden layer neurons, the RootSig function was superior compared to the rest of analyzed activation functions.

scholarly journals An Assessment of a Proposed Hybrid Neural Network for Daily Flow Prediction in Arid Climate

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Milad Jajarmizadeh ◽  
Sobri Harun ◽  
Mohsen Salarpour
Keyword(s):  
Neural Network ◽  
Transfer Functions ◽  
Heuristic Method ◽  
Daily Rainfall ◽  
Hybrid Network ◽  
Coefficient Of Determination ◽  
Training Algorithms ◽  
Rainfall Runoff ◽  
Runoff Simulation ◽  
Hidden Layer

Rainfall-runoff simulation in hydrology using artificial intelligence presents the nonlinear relationships using neural networks. In this study, a hybrid network presented as a feedforward modular neural network (FF-MNN) has been developed to predict the daily rainfall-runoff of the Roodan watershed at the southern part of Iran. This FF-MNN has three layers—input, hidden, and output. The hidden layer has two types of neural expert or module. Hydrometeorological data of the catchment were collected for 21 years. Heuristic method was used to develop the MNN for exploring daily flow generalization. Two training algorithms, namely, backpropagation with momentum and Levenberg-Marquardt, were used. Sigmoid and linear transfer functions were employed to explore the network’s optimum behavior. Cross-validation and predictive uncertainty assessments were carried out to protect overtiring and overparameterization, respectively. Results showed that the FF-MNN could satisfactorily predict stream flow during testing period. The Nash-Sutcliff coefficient, coefficient of determination, and root mean square error obtained using MNN during training and test periods were 0.85, 0.85, and 39.4 and 0.57, 0.58, and 32.2, respectively. The predictive uncertainties for both periods were 0.39 and 0.44, respectively. Generally, the study showed that the FF-MNN can give promising prediction for rainfall-runoff relations.

Healthcare

2019 ◽  
Vol 10 (2) ◽  
pp. 63-85
Author(s):  
Ramani Selvanambi ◽  
Jaisankar N.
Keyword(s):  
Breast Cancer ◽  
Optimization Techniques ◽  
Quality Analysis ◽  
Cancer Information ◽  
Training Algorithms ◽  
Social Spider ◽  
Marquardt Algorithm ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

Quality analysis of the treatment of cancer has been an objective of e-health services for quite some time. The objective is to predict the stage of breast cancer by using diverse input parameters. Breast cancer is one of the main causes of death in women when compared to other tumors. The classification of breast cancer information can be profitable to anticipate diseases or track the hereditary of tumors. For classification, an artificial neural network (ANN) structure was carried out. In the structure, nine training algorithms are used and the proposed is the Levenberg-Marquardt algorithm. For optimizing the hidden layer and neuron, three optimization techniques are used. In the result, the best approval execution is anticipated and the diverse execution evaluation estimation for three optimization algorithms is researched. The correlation execution diagram for an accuracy of 95%, a sensitivity of 98%, and a specificity of 89% of a social spider optimization (SSO) algorithm are shown.

MODELING OF HIGH-SPEED FINISH MILLING PROCESS USING SOFT COMPUTING

2010 ◽  
Vol 01 (03) ◽  
pp. 405-420 ◽  
Author(s):  
B. SUREKHA ◽  
PANDU R. VUNDAVILLI ◽  
M. B. PARAPPAGOUDAR ◽  
K. SHYAM PRASAD
Keyword(s):  
Neural Network ◽  
Soft Computing ◽  
High Speed ◽  
Transfer Functions ◽  
Forward Modeling ◽  
Milling Process ◽  
Rule Base ◽  
Machining Parameters ◽  
Layer Transfer ◽  
Hidden Layer

In the present study, forward modeling of high-speed finish milling process has been solved using soft computing. Two different approaches, namely neural network (NN) and fuzzy logic (FL), have been developed to solve the said problem. The performance of NN and FL systems depends on the structure (i.e. number of neurons in the hidden layer, transfer functions, connection weights, etc.) and knowledge base (i.e. rule base and data base), respectively. Here, an approach is proposed to optimize the above-mentioned parameters of NN and FL systems. A binary coded genetic algorithm (GA) has been used for the said purpose. Once optimized, the NN and FL-based models will be able to provide optimal machining parameters online. The developed approaches are found to solve the above problem effectively, and the performances of the developed approaches have been compared among themselves and with that of the results of existing literature.

Modeling of Gas Holdup and Pressure Drop Using ANN for Gas-Non-Newtonian Liquid Flow in Vertical Pipe

2014 ◽  
Vol 917 ◽  
pp. 244-256 ◽  
Author(s):  
Nirjhar Bar ◽  
Sudip Kumar Das
Keyword(s):  
Pressure Drop ◽  
Liquid Flow ◽  
Multilayer Perceptron ◽  
Transfer Functions ◽  
Gas Holdup ◽  
Newtonian Liquid ◽  
Training Algorithms ◽  
Vertical Pipe ◽  
Frictional Pressure Drop ◽  
Hidden Layer

This paper is an attempt to compare the the performance of the three different Multilayer Perceptron training algorithms namely Backpropagation, Scaled Conjugate Gradient and Levenberg-Marquardt for the prediction of the gas hold up and frictional pressure drop across the vertical pipe for gas non-Newtonian liquid flow from our earlier experimental data. The Multilayer Perceptron consists of a single hidden layer. Four different transfer functions were used in the hidden layer. All three algorithms were useful to predict the gas holdup and frictional pressure drop across the vertical pipe. Statistical analysis using Chi-square test (χ2) confirms that the Backpropagation training algorithm gives the best predictability for both cases.

scholarly journals An Artificial Neural Network Model to Predict the Thermal Properties of Concrete Using Different Neurons and Activation Functions

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Sehmus Fidan ◽  
Hasan Oktay ◽  
Suleyman Polat ◽  
Sarper Ozturk
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Thermal Properties ◽  
Thermal Property ◽  
Building Materials ◽  
Performance Indicator ◽  
Mechanical And Thermal Properties ◽  
Activation Functions ◽  
Marquardt Algorithm ◽  
Artificial Neural

Growing concerns on energy consumption of buildings by heating and cooling applications have led to a demand for improved insulating performances of building materials. The establishment of thermal property for a building structure is the key performance indicator for energy efficiency, whereas high accuracy and precision tests are required for its determination which increases time and experimental costs. The main scope of this study is to develop a model based on artificial neural network (ANN) in order to predict the thermal properties of concrete through its mechanical characteristics. Initially, different concrete samples were prepared, and their both mechanical and thermal properties were tested in accordance with ASTM and EN standards. Then, the Levenberg–Marquardt algorithm was used for training the neural network in the single hidden layer using 5, 10, 15, 20, and 25 neurons, respectively. For each thermal property, various activation functions such as tangent sigmoid functions and triangular basis functions were used to examine the best solution performance. Moreover, a cross-validation technique was used to ensure good generalization and to avoid overtraining. ANN results showed that the best overall R2 performances for the prediction of thermal conductivity, specific heat, and thermal diffusivity were obtained as 0.996, 0.983, and 0.995 for tansig activation functions with 25, 25, and 20 neurons, respectively. The performance results showed that there was a great consistency between the predicted and tested results, demonstrating the feasibility and practicability of the proposed ANN models for predicting the thermal property of a concrete.

System Identification for Nonlinear Maneuvering of Ships Using Neural Network

2010 ◽  
Vol 54 (01) ◽  
pp. 1-14
Author(s):  
G. Rajesh ◽  
G. Giri Rajasekhar ◽  
S. K. Bhattacharyya
Keyword(s):  
Neural Network ◽  
System Identification ◽  
Simulated Data ◽  
Network Models ◽  
Training Data ◽  
Neural Network Models ◽  
The Neural Network ◽  
Marquardt Algorithm ◽  
Hydrodynamic Derivatives ◽  
Hidden Layer

This paper deals with the application of nonparametric system identification to the nonlinear maneuvering of ships using neural network method. The maneuvering equations contain linear as well as nonlinear terms, and one does not attempt to determine the parameters (or hydrodynamic derivatives) associated with nonlinear terms, rather all nonlinear terms are clubbed together to form one unknown time function per equation, which are sought to be represented by neural network coefficients. The time series used in training the network are obtained from simulated data of zigzag and spiral maneuvers. The neural network has one middle or hidden layer of neurons and the Levenberg-Marquardt algorithm is used to obtain the network coefficients. Using the best choices for number of hidden layer neurons, length of training data, convergence tolerance, and so forth, the performances of the proposed neural network models have been investigated and conclusions drawn.

scholarly journals Application of Artificial Neural Network in Simulation of Supercritical Extraction of Valerenic Acid from Valeriana officinalis L.

2012 ◽  
Vol 2012 ◽  
pp. 1-7
Author(s):  
Amir Rabiee Kenaree ◽  
Shohreh Fatemi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Supercritical Extraction ◽  
The Third ◽  
Marquardt Algorithm ◽  
Artificial Neural ◽  
Valerenic Acid ◽  
Hidden Layer ◽  
Dynamic Time ◽  
Mlp Model

Application of artificial neural network (ANN) has been studied for simulation of the extraction process by supercritical CO2. Supercritical extraction of valerenic acid from Valeriana officianalis L. has been studied and simulated according to the significant operational parameters such as pressure, temperature, and dynamic extraction time. ANN, using multilayer perceptron (MLP) model, is employed to predict the amount of extracted VA versus the studied variables. Three tests, validation, and training data sets in three various scenarios are selected to predict the amount of extracted VA at dynamic time of extraction, working pressure, and temperature values. Levenberg-Marquardt algorithm has been employed to train the MLP network. The model in first scenario has three neurons in one hidden layer, and the models associated with the second and the third scenarios have four neurons in one hidden layer. The determination coefficients are calculated as 0.971, 0.940, and 0.964 for the first, second, and the third scenarios, respectively, demonstrating the effectiveness of the MLP model in simulating this process using any of the scenarios, and accurate prediction of extraction yield has been revealed in different working conditions of pressure, temperature, and dynamic time of extraction.

scholarly journals Predicting Hydraulic Conductivity (k) of Tropical Soils by using Artificial Neural Network (ANN)

2009 ◽  
Vol 1 (1) ◽  
pp. 1-6 ◽  
Author(s):  
Lim D.K.H ◽  
Kolay P.K.
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Hydraulic Conductivity ◽  
Conjugate Gradient ◽  
Field Tests ◽  
Tropical Soils ◽  
Training Algorithms ◽  
Marquardt Algorithm ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Hydraulic conductivity of tropical soils is very complex. Several hydraulic conductivity prediction methods have focused on laboratory and field tests, such as the Constant Head Test, Falling Head Test, Ring Infiltrometer, Instantaneous profile method and Test Basins. In the present study, Artificial Neural Network (ANN) has been used as a tool for predicting the hydraulic conductivity (k) of some tropical soils. ANN is potentially useful in situations where the underlying physical process relationships are not fully understood and well-suited in modeling dynamic systems on a real-time basis. The hydraulic conductivity of tropical soil can be predicted by using ANN, if the physical properties of the soil e.g., moisture content, specific gravity, void ratio etc. are known. This study demonstrates the comparison between the conventional estimation of k by using Shepard's equation for approximating k and the predicted k from ANN. A programme was written by using MATLAB 6.5.1 and eight different training algorithms, namely Resilient Backpropagation (rp), Levenberg-Marquardt algorithm (lm), Conjugate Gradient Polak-Ribiere algorithm (cgp), Scale Conjugate Gradient (scg), BFGS Quasi-Newton (bfg), Conjugate Gradient with Powell/Beale Restarts (cgb), Fletcher-Powell Conjugate Gradient (cgf), and One-step Secant (oss) have been compared to produce the best prediction of k. The result shows that the network trained with Resilient Backpropagation (rp) consistently produces the most accurate results with a value of R = 0.8493 and E2 = 0.7209.

RECOGNITION OF THE TEXT BY MEANS OF DEEP LEARNING

2020 ◽  
Vol 69 (1) ◽  
pp. 378-383
Author(s):  
T.A. Nurmukhanov ◽  
◽  
B.S. Daribayev ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Neural Network Model ◽  
Input Data ◽  
Optimization Methods ◽  
Text Recognition ◽  
Activation Functions

Using neural networks, various variations of the classification of objects can be performed. Neural networks are used in many areas of recognition. A big area in this area is text recognition. The paper considers the optimal way to build a network for text recognition, the use of optimal methods for activation functions, and optimizers. Also, the article checked the correctness of text recognition with different optimization methods. This article is devoted to the analysis of convolutional neural networks. In the article, a convolutional neural network model will be trained with a teacher. Teaching with a teacher is a type of training for neural networks in which you provide the input data and the desired result, that is, the student looking at the input data will understand that you need to strive for the result that was provided to him.

BINARY NEURAL NETWORK TRAINING ALGORITHMS BASED ON LINEAR SEQUENTIAL LEARNING

2003 ◽  
Vol 13 (05) ◽  
pp. 333-351 ◽  
Author(s):  
DI WANG ◽  
NARENDRA S. CHAUDHARI
Keyword(s):  
Neural Network ◽  
Sequential Learning ◽  
Training Algorithms ◽  
Neural Network Training ◽  
Neural Network Learning ◽  
Network Learning ◽  
Linear Separability ◽  
Network Training ◽  
Binary Neural Network ◽  
Hidden Layer

A key problem in Binary Neural Network learning is to decide bigger linear separable subsets. In this paper we prove some lemmas about linear separability. Based on these lemmas, we propose Multi-Core Learning (MCL) and Multi-Core Expand-and-Truncate Learning (MCETL) algorithms to construct Binary Neural Networks. We conclude that MCL and MCETL simplify the equations to compute weights and thresholds, and they result in the construction of simpler hidden layer. Examples are given to demonstrate these conclusions.

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