The Effect of Hyper-Parameters on the Performance of Third Order Neural Network Algorithms on Medical Classification Data

2021 ◽  
Vol 02 (01) ◽  
Author(s):  
Nazri Mohd Nawi ◽  
◽  
Prihastuti Harsani ◽  
Eneng Tita Tosida ◽  
Khairina Mohamad Roslan ◽  
...  
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Numerical Data ◽  
Bp Algorithm ◽  
Back Propagation Algorithm ◽  
Classification Problems ◽  
Third Order ◽  
Learning Speed ◽  
Medical Classification ◽  
Artificial Neural Network Ann

The artificial neural network (ANN) particularly back propagation (BP) algorithm has recently been applied in many areas. It is known that BP is an excellent classifier for nonlinear input and output numerical data. However, the popularity of BP comes with some drawbacks such as slow in learning and easily getting stuck in local minima. Improving training efficiency of BP algorithm is an active area of research and numerous papers have been reviewed in the literature. Furthermore, the performance of BP algorithm also highly influenced by the size of the datasets and the data preprocessing techniques that been chosen. This paper presents an improvement of BP by adjusting the two term parameters on the performance of third order neural network methods. This work also demonstrates the advantages of using preprocessing dataset in order to improve the BP convergence. The efficiency of the proposed method is verified by means of simulation on medical classification problems. The results show that the proposed implementation significantly improves the learning speed of the general back-propagation algorithm.

Method of Wind Power Prediction Based on ANN and Improvements

2014 ◽  
Vol 521 ◽  
pp. 143-146 ◽  
Author(s):  
Shao Shuai Song ◽  
Ran Li
Keyword(s):  
Neural Network ◽  
Wind Power ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Bp Algorithm ◽  
Good Effect ◽  
Power Prediction ◽  
Learning Speed ◽  
Wind Power Prediction ◽  
Artificial Neural Network Ann

This paper puts emphasis on studying on artificial neural network (ANN) method. Following that a model of wind power prediction is established based on back-propagation neural network. In order to improve the learning speed of ANN, a revised BP algorithm is adopted by using variable step and the combination of GA and BP algorithm. This method has a good effect in practice.

Analysis of artificial neural network performance based on influencing factors for temperature forecasting applications

2020 ◽  
Vol 26 (3) ◽  
pp. 209-223
Author(s):  
M. Madhiarasan ◽  
M. Tipaldi ◽  
P. Siano
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Influencing Factors ◽  
Network Performance ◽  
Back Propagation ◽  
Back Propagation Algorithm ◽  
Research Fields ◽  
Temperature Forecasting ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Artificial neural network (ANN)-based methods belong to one of the most growing research fields within the artificial intelligence ecosystem, and many novel contributions have been developed over the last years. They are applied in many contexts, although some “influencing factors” such as the number of neurons, the number of hidden layers, and the learning rate can impact the performance of the resulting artificial neural network-based applications. This paper provides a deep analysis about artificial neural network performance based on such factors for real-world temperature forecasting applications. An improved back propagation algorithm for such applications is also presented. By using the results of this paper, researchers and practitioners can analyse the encountered issues when applying ANN-based models for their own specific applications with the aim of achieving better performance indexes.

Simultaneous Quantitative Analysis of Clorsulon and Ivermectin in a Veterinary Formulation by Artificial Neural Network

2008 ◽  
Vol 59 (10) ◽  
Author(s):  
Gozde Pektas ◽  
Erdal Dinc ◽  
Dumitru Baleanu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Transfer Function ◽  
Back Propagation ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

Simultaneaous spectrophotometric determination of clorsulon (CLO) and invermectin (IVE) in commercial veterinary formulation was performed by using the artificial neural network (ANN) based on the back propagation algorithm. In order to find the optimal ANN model various topogical networks were tested by using different hidden layers. A logsig input layer, a hidden layer of neurons using the logsig transfer function and an output layer of two neurons with purelin transfer function was found suitable for basic configuration for ANN model. A calibration set consisting of CLO and IVE in calibration set was prepared in the concentration range of 1-23 �g/mL and 1-14 �g/mL, repectively. This calibration set contains 36 different synthetic mixtures. A prediction set was prepared in order to evaluate the recovery of the investigated approach ANN chemometric calibration was applied to the simultaneous analysis of CLO and IVE in compounds in a commercial veterinary formulation. The experimental results indicate that the proposed method is appropriate for the routine quality control of the above mentioned active compounds.

scholarly journals Prediction of Drape Coefficient by Artificial Neural Network

2015 ◽  
Vol 15 (4) ◽  
pp. 266-274 ◽  
Author(s):  
Adel Ghith ◽  
Thouraya Hamdi ◽  
Faten Fayala
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Propagation Algorithm ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer ◽  
Different Diameters

Abstract An artificial neural network (ANN) model was developed to predict the drape coefficient (DC). Hanging weight, Sample diameter and the bending rigidities in warp, weft and skew directions are selected as inputs of the ANN model. The ANN developed is a multilayer perceptron using a back-propagation algorithm with one hidden layer. The drape coefficient is measured by a Cusick drape meter. Bending rigidities in different directions were calculated according to the Cantilever method. The DC obtained results show a good correlation between the experimental and the estimated ANN values. The results prove a significant relationship between the ANN inputs and the drape coefficient. The algorithm developed can easily predict the drape coefficient of fabrics at different diameters.

An Improved Artificial Neural Network for Recognition of Beam Pump Dynamometer Card

2012 ◽  
Vol 524-527 ◽  
pp. 1331-1334
Author(s):  
Jun Ni ◽  
Zhan Li Ren ◽  
Guo Qing Han
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Linear Mapping ◽  
Back Propagation Algorithm ◽  
Propagation Algorithm ◽  
Beam Pump ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Dynamometer Card

Beam pump dynamometer card plays an important role in identifying the production state of oil wells. With an ability to reflect any non-linear mapping relationship, the artificial neural network (ANN) can be used in pattern recognition. This paper illuminates ANN realization in identifying fault kinds of dynamometer cards, including a back-propagation algorithm, characteristics of the Dynamometer card and some examples. It is concluded that the buildup of a neural network and the abstract of dynamometer cards are important to successful application.

scholarly journals Prediction of Neutron Yield of IR-IECF Facility in High Voltages Using Artificial Neural Network

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
A. Sadighzadeh ◽  
A. Salehizadeh ◽  
M. Mohammadzadeh ◽  
F. Shama ◽  
S. Setayeshi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Discharge Current ◽  
Back Propagation ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Cathode Voltage ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Maximum Voltage

Artificial neural network (ANN) is applied to predict the number of produced neutrons from IR-IECF device in wide discharge current and voltage ranges. Experimentally, discharge current from 20 to 100 mA had been tuned by deuterium gas pressure and cathode voltage had been changed from −20 to −82 kV (maximum voltage of the used supply). The maximum neutron production rate (NPR) of 1.46 × 107 n/s had occurred when the voltage was −82 kV and the discharge current was 48 mA. The back-propagation algorithm is used for training of the proposed multilayer perceptron (MLP) neural network structure. The obtained results show that the proposed ANN model has achieved good agreement with the experimental data. Results show that NPR of 1.855 × 108 n/s can be achieved in voltage and current of 125 kV and 45 mA, respectively. This prediction shows 52% increment in maximum voltage of power supply. Also, the optimum discharge current can increase 1270% NPR.

A Modified Back Propagation Algorithm of Neural Network with Global Optimization

2014 ◽  
Vol 1042 ◽  
pp. 232-238
Author(s):  
Jing Jing Li ◽  
Zhe Cui
Keyword(s):  
Neural Network ◽  
Global Optimization ◽  
Optimization Algorithm ◽  
Back Propagation ◽  
Bp Algorithm ◽  
Efficient Global Optimization ◽  
Back Propagation Algorithm ◽  
Global Optimization Algorithm ◽  
Function Fitting ◽  
Neural Network Algorithm

The advantages and weakens of traditional BP algorithm is briefly analyzed and an efficient global optimization algorithm is proposed.The basic principle of the algorithm is presented,and a new BP neural network algorithm based on the existing BP algorithm and the new global optimization algorithm is proposed, considering the new global optimization algorithm can solve the problem of local minimum efficiently. To verify the effectiveness of the new BP algorithm,the paper compared the experimental results of various algorithms in solving function fitting problem.

scholarly journals APPLICABILITY OF ARTIFICIAL NEURAL NETWORK MODEL FOR SIMULATION OF MONTHLY RUNOFF IN COMPARISON WITH SOM OTHER TRADITIONAL MODELS

2009 ◽  
Vol 12 (4) ◽  
pp. 94-106 ◽  
Author(s):  
Duc Van Le
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Research Trend ◽  
Rainfall Runoff ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Runoff Forecasting ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Artificial Neural Network (ANN) model along with Back Propagation Algorithm (BPA) has been applied in many fields, especially in hydrology and water resources management to simulate or forecast rainfall runoff process, discharge and water level - time series, and other hydrological variables. Several researches have recently been focusing to compare the applicability of ANN model with other theory-driven and data-driven approaches. The comparison of ANN with M5 model trees for rainfall-runoff forecasting, with ARMAX models for deriving flow series, with AR models and regression models for forecasting and estimating daily river flows have been carried out. The better results that were implemented by ANN model have been concluded. So, this research trend is continued for the comparison of ANN model with Tank, Harmonic, Thomas and Fiering models in simulation of the monthly runoffs at Dong Nai river basin, Viet Nam. The results proved ANN being the best choice among these models, if suitable and enough data sources were available.

scholarly journals Artificial Neural Network Approach for Solving Fuzzy Fractional order initial value problems under H-differentiability

2020 ◽  
Author(s):  
Somayeh Ezadi ◽  
Tofigh Allahviranloo
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Initial Conditions ◽  
Error Function ◽  
Back Propagation ◽  
Back Propagation Algorithm ◽  
Neural Network Approach ◽  
Error Back Propagation ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

This paper aims to solve the celebrated Fuzzy Fractional Differential Equations (FFDE) using an Artificial Neural Network (ANN) technique. Compared to the integer order differential equation, the proposed FFDE can better describe several real application problems of various physical systems. To accomplish the aforementioned aim, the error back propagation algorithm and a multi-layer feed forward neural architecture are utilized using the unsupervised learning in order to minimize the error function as well as the modification of the parameters such as weights and biases. By combining the initial conditions with the ANN, output provides an appropriate approximate solution of the proposed FFDE. Then, two illustrative examples are solved to confirm the applicability of the concept as well as to demonstrate both the precision and effectiveness of the developed method. By comparing with some traditional methods, the obtained results reveals a close match that confirms both accuracy and correctness of the proposed method.

scholarly journals A New Hybrid Grasshopper Optimization - Backpropagation for Feedforward Neural Network Training

2020 ◽  
Vol 25 (1) ◽  
pp. 118
Author(s):  
Samer Alsammarraie ◽  
Nazar K. Hussein
Keyword(s):  
Neural Network ◽  
Optimization Algorithm ◽  
Hybrid Algorithm ◽  
Back Propagation ◽  
Global Optimum ◽  
Bp Algorithm ◽  
Back Propagation Algorithm ◽  
Grasshopper Optimization Algorithm ◽  
Grasshopper Optimization ◽  
Searching Ability

The Grasshopper optimization algorithm showed a rapid converge in the initial phases of the global search, however while being around the global optimum, the searching process became so slow. On the contrary, the gradient descending method around achieved faster convergent speed global optimum, and the convergent accuracy was showed to be higher at the same time. As a result, the proposed hybrid algorithm combined Grasshopper optimization algorithm (GOA) along with the back-propagation (BP) algorithm, also referred to as GOA–BP algorithm, was introduced to provide training to the weights of the feed forward neural network (FNN), the proposed hybrid algorithm can utilize the strong global searching ability of the GOA, and the intense local searching ability of the Back-Propagation algorithm. The results of experiments showed that the proposed hybrid GOA–BP algorithm was better and faster in convergent speed and accuracy than the Grasshopper optimization algorithm (GOA) and BP algorithm.   http://dx.doi.org/10.25130/tjps.25.2020.018  

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