A Novel Hybrid Model Using RBF and PSO for Net Asset Value Prediction

2018 ◽  
pp. 1031-1049 ◽  
Author(s):  
C. M. Anish ◽  
Babita Majhi ◽  
Ritanjali Majhi
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Performance Measure ◽  
Neural Network Models ◽  
Proposed Model ◽  
Derivative Free ◽  
Asset Value ◽  
Net Asset Value ◽  
Small Investors ◽  
The Cost

Net asset value (NAV) prediction is an important area of research as small investors are doing investment in there, Literature survey reveals that very little work has been done in this field. The reported literature mainly used various neural network models for NAV prediction. But the derivative based learning algorithms of these reported models have the problem of trapping into the local solution. Hence in chapter derivative free algorithm, particle swarm optimization is used to update the parameters of radial basis function neural network for prediction of NAV. The positions of particles represent the centers, spreads and weights of the RBF model and the minimum MSE is used as the cost function. The convergence characteristics are obtained to show the performance of the model during training phase. The MAPE and RMSE value are calculated during testing phase to show the performance of the proposed RBF-PSO model. These performance measure exhibits that the proposed model is better model in comparison to MLANN, FLANN and RBFNN models.

A Novel Hybrid Model Using RBF and PSO for Net Asset Value Prediction

2018 ◽  
pp. 54-72
Author(s):  
C. M. Anish ◽  
Babita Majhi ◽  
Ritanjali Majhi
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Performance Measure ◽  
Neural Network Models ◽  
Proposed Model ◽  
Derivative Free ◽  
Asset Value ◽  
Net Asset Value ◽  
Small Investors ◽  
The Cost

Net asset value (NAV) prediction is an important area of research as small investors are doing investment in there, Literature survey reveals that very little work has been done in this field. The reported literature mainly used various neural network models for NAV prediction. But the derivative based learning algorithms of these reported models have the problem of trapping into the local solution. Hence in chapter derivative free algorithm, particle swarm optimization is used to update the parameters of radial basis function neural network for prediction of NAV. The positions of particles represent the centers, spreads and weights of the RBF model and the minimum MSE is used as the cost function. The convergence characteristics are obtained to show the performance of the model during training phase. The MAPE and RMSE value are calculated during testing phase to show the performance of the proposed RBF-PSO model. These performance measure exhibits that the proposed model is better model in comparison to MLANN, FLANN and RBFNN models.

Identification of Tomato Leaf Disease Detection using Pretrained Deep Convolutional Neural Network Models

2020 ◽  
Vol 21 (4) ◽  
pp. 625-635
Author(s):  
Anandhakrishnan T ◽  
Jaisakthi S.M Murugaiyan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Early Stage ◽  
Computing Time ◽  
Network Models ◽  
Deep Convolutional Neural Network ◽  
Neural Network Models ◽  
Leaf Disease ◽  
Proposed Model ◽  
Deep Cnn

In this paper, we proposed a plant leaf disease identification model based on a Pretrained deep convolutional neural network (Deep CNN). The Deep CNN model is trained using an open dataset with 10 different classes of tomato leaves We observed that overall architectures which can increase the best performance of the model. The proposed model was trained using different training epochs, batch sizes and dropouts. The Xception has attained maximum accuracy compare with all other approaches. After an extensive simulation, the proposed model achieves classification accuracy better. This accuracy of the proposed work is greater than the accuracy of all other Pretrained approaches. The proposed model is also tested with respect to its consistency and reliability. The set of data used for this work was collected from the plant village dataset, including sick and healthy images. Models for detection of plant disease should predict the disease quickly and accurately in the early stage itself so that a proper precautionary measures can be applied to avoid further spread of the diseases. So, to reduce the main issue about the leaf diseases, we can analyze distinct kinds of deep neural network architectures in this research. From the outcomes, Xception has a constantly improving more to enhance the accuracy by increasing the number of epochs, without any indications of overfitting and decreasein quality. And Xception also generated a fine 99.45% precision in less computing time.

scholarly journals A Novel CapsNet based Image Reconstruction and Regression Analysis

2020 ◽  
Vol 2 (3) ◽  
pp. 156-164 ◽  
Author(s):  
Dr. Akey Sungheetha ◽  
Dr. Rajesh Sharma R
Keyword(s):  
Neural Network ◽  
Regression Analysis ◽  
Network Models ◽  
Superior Performance ◽  
Neural Network Models ◽  
Innovative Method ◽  
Proposed Model ◽  
Retrieval Efficiency ◽  
High Level ◽  
Better Than

In the field of image processing, all types of computation models are almost evolved to solve the issues through encoded neurons. However, compared with decoding orientation and regression analysis, still the doors are open due to its complexity. At present technologies uses two steps such as, decoding the intermediate terms and reconstruction using decoded information. The performance in terms of regression analysis is lagging due to the decoded intermediate terms. Conventional neural network models perform better in feature classification and representation, though the performance is reduced while handling high level features. Considering these issues in image classification and regression, the proposed model is designed with capsule network as an innovative method which is suitable to handle high level features. The experimental results of the proposed model are compared with conventional neural network models such as BPNN and CNN to validate the superior performance. The proposed model achieves better retrieval efficiency of 95.4% which is much better than other neural network models.

scholarly journals Disease Identification in Chilli Leaves using Machine Learning Techniques

2019 ◽  
Vol 9 (1S3) ◽  
pp. 325-329
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Detection System ◽  
Network Models ◽  
Detection Algorithm ◽  
Machine Learning Techniques ◽  
Neural Network Models ◽  
The Past ◽  
Learning Techniques ◽  
Proposed Model

Crop diseases reduce the yield of the crop or may even kill it. Over the past two years, as per the I.C.A.R, the production of chilies in the state of Goa has reduced drastically due to the presence of virus. Most of the plants flower very less or stop flowering completely. In rare cases when a plant manages to flower, the yield is substantially low. Proposed model detects the presence of disease in crops by examining the symptoms. The model uses an object detection algorithm and supervised image recognition and feature extraction using convolutional neural network to classify crops as infected or healthy. Google machine learning libraries, TensorFlow and Keras are used to build neural network models. An Android application is developed around the model for the ease of using the disease detection system.

scholarly journals Deep exploration networks for rapid engineering of functional DNA sequences

2019 ◽  
Author(s):  
Johannes Linder ◽  
Nicholas Bogard ◽  
Alexander B. Rosenberg ◽  
Georg Seelig
Keyword(s):  
Neural Network ◽  
Dna Sequences ◽  
Network Models ◽  
Regulatory Elements ◽  
Differential Splicing ◽  
Neural Network Models ◽  
Gradient Ascent ◽  
Functional Dna ◽  
The Cost ◽  
Deep Exploration

Engineering gene sequences with defined functional properties is a major goal of synthetic biology. Deep neural network models, together with gradient ascent-style optimization, show promise for sequence generation. The generated sequences can however get stuck in local minima, have low diversity and their fitness depends heavily on initialization. Here, we develop deep exploration networks (DENs), a type of generative model tailor-made for searching a sequence space to minimize the cost of a neural network fitness predictor. By making the network compete with itself to control sequence diversity during training, we obtain generators capable of sampling hundreds of thousands of high-fitness sequences. We demonstrate the power of DENs in the context of engineering RNA isoforms, including polyadenylation and cell type-specific differential splicing. Using DENs, we engineered polyadenylation signals with more than 10-fold higher selection odds than the best gradient ascent-generated patterns and identified splice regulatory elements predicted to result in highly differential splicing between cell lines.

Estimation of the Temperatures in an Experimental Infrared Heated Greenhouse Using Neural Network Models

2013 ◽  
Vol 4 (2) ◽  
pp. 14-22 ◽  
Author(s):  
Angeliki Kavga ◽  
Vassilis Kappatos
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Network Models ◽  
Neural Network Models ◽  
High Quality ◽  
The Neural Network ◽  
The Cost ◽  
Greenhouse Control ◽  
Good Agreement ◽  
Indoor And Outdoor

A high quality greenhouse control demands continuous measurements of indoor and outdoor greenhouse conditions. In this work, neural network models were used for reducing the cost and the time of temperature measurements, estimating two of the most important parameters in the operation of the greenhouse, namely the inside air temperature and the cover temperature of greenhouse. An extensive experimental investigation was carried out in an infrared heated greenhouse, using the experimental data to train and validate the neural network models. The modelling results show that the estimated temperatures have been in good agreement with the experimental data with accuracy ranging from 95.64% to 97.67%.

scholarly journals A Fast and Accurate Wind Speed and Direction Nowcasting Model for Renewable Energy Management Systems

Energies ◽  
2021 ◽  
Vol 14 (23) ◽  
pp. 7878
Author(s):  
Saira Al-Zadjali ◽  
Ahmed Al Maashri ◽  
Amer Al-Hinai ◽  
Rashid Al Abri ◽  
Swaroop Gajare ◽  
...  
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Wind Power ◽  
Wind Farm ◽  
Network Models ◽  
Training Data ◽  
Neural Network Models ◽  
Energy Management Systems ◽  
Proposed Model ◽  
Power Utilities

To plan operations and avoid any grid disturbances, power utilities require accurate power generation estimates for renewable generation. The generation estimates for wind power stations require an accurate prediction of wind speed and direction. This paper proposes a new prediction model for nowcasting the wind speed and direction, which can be used to predict the output of a wind power plant. The proposed model uses perturbed observations to train the ensemble networks. The trained model is then used to predict the wind speed and direction. The paper performs a comparative assessment of three artificial neural network models. It also studies the performance of introducing perturbed observations to the model using six different interpolation techniques. For each technique, the computational efficiency is measured and assessed. Furthermore, the paper presents an exhaustive investigation of the performance of neural network types and several techniques in training, data splitting, and interpolation. To check the efficacy of the proposed model, the power output from a real wind farm is predicted and compared with the actual recorded measurements. The results of the comprehensive analysis show that the proposed model outperforms contending models in terms of accuracy and execution time. Therefore, this model can be used by operators to reliably generate a dispatch plan.

Method of complex copper-zinc ore typification using neural network models

2020 ◽  
Vol 5 ◽  
pp. 140-147 ◽  
Author(s):  
T.N. Aleksandrova ◽  
◽  
E.K. Ushakov ◽  
A.V. Orlova ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Neural Network Models ◽  
Copper Zinc ◽  
Complex Copper

Digital twin of equipment as a basis for the consumer in digital production

2020 ◽  
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Chip Formation ◽  
Network Models ◽  
Machining Accuracy ◽  
Neural Network Models ◽  
Digital Twin ◽  
The Neural Network ◽  
Digital Production ◽  
Cyberphysical System

The neural network models series used in the development of an aggregated digital twin of equipment as a cyber-physical system are presented. The twins of machining accuracy, chip formation and tool wear are examined in detail. On their basis, systems for stabilization of the chip formation process during cutting and diagnose of the cutting too wear are developed. Keywords cyberphysical system; neural network model of equipment; big data, digital twin of the chip formation; digital twin of the tool wear; digital twin of nanostructured coating choice

Sign in / Sign up

Export Citation Format

Share Document