scholarly journals Modified neural network based cascaded control for product composition of reactive distillation

2016 ◽  
Vol 18 (2) ◽  
pp. 111-121 ◽  
Author(s):  
Vandana Sakhre ◽  
Sanjeev Jain ◽  
V. S. Sapkal ◽  
D.P. Agarwal
Keyword(s):  
Neural Network ◽  
Mean Square Error ◽  
Control Strategy ◽  
Reactive Distillation ◽  
Descent Method ◽  
Steepest Descent ◽  
Steepest Descent Method ◽  
Mean Square ◽  
Loop Control ◽  
Single Loop

Abstract In this research work, neural network based single loop and cascaded control strategies, based on Feed Forward Neural Network trained with Back Propagation (FBPNN) algorithm is carried out to control the product composition of reactive distillation. The FBPNN is modified using the steepest descent method. This modification is suggested for optimization of error function. The weights connecting the input and hidden layer, hidden and output layer is optimized using steepest descent method which causes minimization of mean square error and hence improves the response of the system. FBPNN, as the inferential soft sensor is used for composition estimation of reactive distillation using temperature as a secondary process variable. The optimized temperature profile of the reactive distillation is selected as input to the neural network. Reboiler heat duty is selected as a manipulating variable in case of single loop control strategy while the bottom stage temperature T9 is selected as a manipulating variable for cascaded control strategy. It has been observed that modified FBPNN gives minimum mean square error. It has also been observed from the results that cascaded control structure gives improved dynamic response as compared to the single loop control strategy.

RELATIONSHIP BETWEEN A FUZZY LOGIC AND A STEEPEST DESCENT APPROACH TO OPTIMIZE A FEEDFORWARD ARTIFICIAL NEURAL NETWORK CONFIGURATION

1994 ◽  
Vol 05 (04) ◽  
pp. 299-312
Author(s):  
ROBERT N. SHARPE ◽  
MO-YUEN CHOW
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Mathematical Models ◽  
Cost Function ◽  
Evaluation Criteria ◽  
Descent Method ◽  
Steepest Descent ◽  
Steepest Descent Method ◽  
Network Configuration ◽  
Training Time

The neural network designer must take into consideration many factors when selecting an appropriate network configuration. The performance of a given network configuration is influenced by many different factors such as: accuracy, training time, sensitivity, and the number of neurons used in the implementation. Using a cost function based on the four criteria mentioned previously, the various network paradigms can be evaluated relative to one another. If the mathematical models of the evaluation criteria as functions of the network configuration are known, then traditional techniques (such as the steepest descent method) could be used to determine the optimal network configuration. The difficulty in selecting an appropriate network configuration is due to the difficulty involved in determining the mathematical models of the evaluation criteria. This difficulty can be avoided by using fuzzy logic techniques to perform the network optimization as opposed to the traditional techniques. Fuzzy logic avoids the need of a detailed mathematical description of the relationship between the network performance and the network configuration, by using heuristic reasoning and linguistic variables. A comparison will be made between the fuzzy logic approach and the steepest descent method for the optimization of the cost function. The fuzzy optimization procedure could be applied to other systems where there is a priori information about their characteristics.

scholarly journals Hessian with Mini-Batches for Electrical Demand Prediction

2020 ◽  
Vol 10 (6) ◽  
pp. 2036 ◽  
Author(s):  
Israel Elias ◽  
José de Jesús Rubio ◽  
David Ricardo Cruz ◽  
Genaro Ochoa ◽  
Juan Francisco Novoa ◽  
...  
Keyword(s):  
Neural Network ◽  
Descent Method ◽  
Steepest Descent ◽  
Steepest Descent Method ◽  
Demand Prediction ◽  
The Neural Network ◽  
Electrical Demand

The steepest descent method is frequently used for neural network tuning. Mini-batches are commonly used to get better tuning of the steepest descent in the neural network. Nevertheless, steepest descent with mini-batches could be delayed in reaching a minimum. The Hessian could be quicker than the steepest descent in reaching a minimum, and it is easier to achieve this goal by using the Hessian with mini-batches. In this article, the Hessian is combined with mini-batches for neural network tuning. The discussed algorithm is applied for electrical demand prediction.

Detection of Biomedical Images by Using Bio-inspired Artificial Intelligent

2020 ◽  
Vol 38 (2A) ◽  
pp. 255-264
Author(s):  
Hanan A. R. Akkar ◽  
Sameem A. Salman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mean Square Error ◽  
Average Rate ◽  
Mean Square ◽  
Performance Measurements ◽  
Artificial Intelligent ◽  
Average Mean Square Error ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Computer vision and image processing are extremely necessary for medical pictures analysis. During this paper, a method of Bio-inspired Artificial Intelligent (AI) optimization supported by an artificial neural network (ANN) has been widely used to detect pictures of skin carcinoma. A Moth Flame Optimization (MFO) is utilized to educate the artificial neural network (ANN). A different feature is an extract to train the classifier. The comparison has been formed with the projected sample and two Artificial Intelligent optimizations, primarily based on classifier especially with, ANN-ACO (ANN training with Ant Colony Optimization (ACO)) and ANN-PSO (training ANN with Particle Swarm Optimization (PSO)). The results were assessed using a variety of overall performance measurements to measure indicators such as Average Rate of Detection (ARD), Average Mean Square error (AMSTR) obtained from training, Average Mean Square error (AMSTE) obtained for testing the trained network, the Average Effective Processing Time (AEPT) in seconds, and the Average Effective Iteration Number (AEIN). Experimental results clearly show the superiority of the proposed (ANN-MFO) model with different features.

Modelling response of infiltration loss toward water table depth using RBFN, RNN, ANFIS techniques

2021 ◽  
Vol 25 (2) ◽  
pp. 227-234
Author(s):  
Sandeep Samantaray ◽  
Abinash Sahoo
Keyword(s):  
Neural Network ◽  
Mean Square Error ◽  
Water Table ◽  
Environmental Sustainability ◽  
Model Development ◽  
Water Table Depth ◽  
Water Levels ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Inference System

Accurate prediction of water table depth over long-term in arid agricultural areas are very much important for maintaining environmental sustainability. Because of intricate and diverse hydrogeological features, boundary conditions, and human activities researchers face enormous difficulties for predicting water table depth. A virtual study on forecast of water table depth using various neural networks is employed in this paper. Hybrid neural network approach like Adaptive Neuro Fuzzy Inference System (ANFIS), Recurrent Neural Network (RNN), Radial Basis Function Neural Network (RBFN) is employed here to appraisal water levels as a function of average temperature, precipitation, humidity, evapotranspiration and infiltration loss data. Coefficient of determination (R2), Root mean square error (RMSE), and Mean square error (MSE) are used to evaluate performance of model development. While ANFIS algorithm is used, Gbell function gives best value of performance for model development. Whole outcomes establish that, ANFIS accomplishes finest as related to RNN and RBFN for predicting water table depth in watershed.

scholarly journals Predicting the discharge coefficient of oblique cylindrical weir using neural network techniques

2021 ◽  
Vol 14 (16) ◽  
Author(s):  
Adnan A. Ismael ◽  
Saleh J. Suleiman ◽  
Raid Rafi Omar Al-Nima ◽  
Nadhir Al-Ansari
Keyword(s):  
Neural Network ◽  
Mean Square Error ◽  
Radial Basis Function Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Superior Performance ◽  
Mean Square ◽  
The Third ◽  
Testing Stage ◽  
Inclination Angles

AbstractCylindrical weir shapes offer a steady-state overflow pattern, where the type of weirs can offer a simple design and provide the ease-to-pass floating debris. This study considers a coefficient of discharge (Cd) prediction for oblique cylindrical weir using three diameters, the first is of D1 = 0.11 m, the second is of D2 = 0.09 m, and the third is of D3 = 0.06.5 m, and three inclination angles with respect to channel axis, the first is of θ1 = 90 ͦ, the second is of θ2 = 45 ͦ, and the third is of θ3 = 30 ͦ. The Cd values for total of 56 experiments are estimated by using the radial basis function network (RBFN), in addition of comparing that with the back-propagation neural network (BPNN) and cascade-forward neural network (CFNN). Root mean square error (RMSE), mean square error (MSE), and correlation coefficient (CC) statics are used as metrics measurements. The RBFN attained superior performance comparing to the other neural networks of BPNN and CFNN. It is found that, for the training stage, the RBFN network benchmarked very small RMSE and MSE values of 1.35E-12 and 1.83E-24, respectively and for the testing stage, it also could benchmark very small RMSE and MSE values of 0.0082 and 6.80E-05, respectively.

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