scholarly journals Solving fuzzy volterra-fredholm integral equation by fuzzy artificial neural network

2021 ◽  
Vol 0 (0) ◽  
pp. 0
Author(s):  
Seiyed Hadi Abtahi ◽  
Hamidreza Rahimi ◽  
Maryam Mosleh
Keyword(s):  
Neural Network ◽  
Integral Equation ◽  
Artificial Neural Network ◽  
Fredholm Integral Equation ◽  
The Other ◽  
Programming Algorithm ◽  
Effective Solution ◽  
Equation Modelling ◽  
Engineering Problems ◽  
Artificial Neural

<p style='text-indent:20px;'>The volterra-fredholm integral equation in all forms are arose from physics, biology and engineering problems which is derived from differential equation modelling. On the other hand, the trained programming algorithm by the fuzzy artificial neural networks has effective solution to find the best answer. In this article we try to estimate the equation and its answer by developed fuzzy artificial neural network to fuzzy volterra-fredholme integral. Our attempts would lead to benchmark other extended forms of this type of equation.</p>

scholarly journals Analysis of Crowd Flow Parameters Using Artificial Neural Network

2018 ◽  
Vol 19 (4) ◽  
pp. 335-345
Author(s):  
Poojari Yugendar ◽  
K.V.R. Ravishankar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Absolute Error ◽  
The Other ◽  
Ann Model ◽  
Deterministic Models ◽  
Accuracy Measurement ◽  
Flow Parameters ◽  
Non Linear ◽  
Artificial Neural

Abstract Research scientists have been developing mathematical tools to detect objects, recognize objects and actions, and discover behaviours and events to human abilities. In all these efforts, the understanding of human actions is of a special interest for both application and research purposes. In this study, crowd flow parameters are analysed by considering linear and non linear relationships between stream flow parameters using conventional and soft computing approach. Deterministics models like Greenshield and Underwood were applied in the study to describe flow characteristics. A non-linear model based on Artificial Neural Network (ANN) approach is also used to build a relationship between different crowd flow parameters and compared with the other deterministic models. ANN model gave good results based on accuracy measurement to deterministic models because of their self-processing and intelligent behaviour. Mean absolute error (MAE) and root mean square error (RMSE) values for the best fitted ANN model are less than those for the other models. ANN model gives better performance in fitness of model and future prediction of flow parameters.

scholarly journals Smart Routing Protocol Algorithm Using Fuzzy Artificial Neural Network OSPF

2021 ◽  
pp. 155-160
Author(s):  
Sami Hasan ◽  
Abdulhakeem Amer
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Routing Protocol ◽  
Minimum Weight ◽  
The Other ◽  
Transmission Time ◽  
Fuzzy Membership ◽  
Optimum Path ◽  
Artificial Neural ◽  
Network Overlap

The OSPF cost is proportionally indicated the transmitting packet overhead through a certain interface and inversely proportional to the interface bandwidth. Thus, this cost may minimized by direct packet transmitting to the other side via various probable paths simultaneously. Logically, the minimum weight path is the optimum path. This paper propose a novel Fuzzy Artificial Neural Network to create Smart Routing Protocol Algorithm. Consequently, the Fuzzy Artificial Neural Network Overlap has been reduced from (0.883 ms) to (0.602 ms) at fuzzy membership 1.5 to 4.5 respectively. This indicated the transmission time is two-fold faster than the standard overlapping time (1.3 ms).

scholarly journals The Underlying Drivers of Underprivileged Households’ Intention and Behavior towards Community Forestry Management: A Study Using Structural Equation Modelling and Artificial Neural Network Approach

2020 ◽  
Vol 12 (18) ◽  
pp. 7330
Author(s):  
Abdullah Al Mamun ◽  
Syed Ali Fazal ◽  
Muhammad Mehedi Masud ◽  
Ganeshsree Selvachandran ◽  
Noor Raihani Zainol ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Structural Equation Modelling ◽  
Structural Equation ◽  
Community Forestry ◽  
Significant Positive Effect ◽  
Equation Modelling ◽  
Forestry Management ◽  
Artificial Neural ◽  
Positive Effect

In acknowledging the significant role of forestry on the environmental, social, and economic sustainability of local communities, this study focused on examining how different factors affect the intentional behavior towards community forestry among the poor households in Malaysia. Employing theory of planned behavior (TPB) in an expanded model, this study collected data from 420 underprivileged households from 10 states in Malaysia using a survey questionnaire. Final analysis is performed using two methods, one being the well-established, conventional way of partial least square–structural equation modelling (PLS-SEM); the other being a frontier technology of computing using artificial neural network (ANN), which is generated through a deep learning algorithm to achieve the maximum possible accuracy for each of the five scenarios aforementioned. The study found that perceived benefits (PB) and eco-literacy (EL) have a significant positive effect on the attitude towards environment (ATE) while normative belief (NB) and motivation (MO) have a significant positive effect on subjective norms (SUN). Perceived control (PC) has a significant positive effect on perceived behavioral control (PBC). ATE, SUN, and PBC have a significant positive effect on the intention towards community forestry (ITCF), whereas the ITCF has a significant positive effect on community forestry adoption behavior (CFAB). When formulating and enforcing carbon reduction and poverty elevating programs through community forestry, the Malaysian government should consider the perceptions of poor families and the prerogative from their special reference groups to enhance the perceived ability of the vulnerable groups for positive and effective pro-environmental behavior that can lead to sustainable forestry management.

AN ARTIFICIAL NEURAL NETWORK ALGORITHM FOR DYNAMIC PROGRAMMING

1990 ◽  
Vol 01 (03) ◽  
pp. 211-220 ◽  
Author(s):  
Chinchuan Chiu ◽  
Chia-Yiu Maa ◽  
Michael A. Shanblatt
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Dynamic Programming ◽  
Optimal Path ◽  
Dynamic Programming Algorithm ◽  
Performance Measure ◽  
Programming Algorithm ◽  
Network Dynamic ◽  
Destination Node ◽  
Artificial Neural

An artificial neural network (ANN) formulation for solving the dynamic programming problem (DPP) is presented. The DPP entails finding an optimal path from a source node to a destination node which minimizes (or maximizes) a performance measure of the problem. The optimization procedure is implemented and demonstrated using a modified Hopfield–Tank ANN. Simulations show that the ANN can provide a near-optimal solution during an elapsed time of only a few characteristic time constants of the circuit for DPPs with sizes as large as 64 stages with 64 states in each stage. An application of the proposed algorithm to an optimal control problem is presented. The proposed artificial neural network dynamic programming algorithm is attractive due to its radically improved speed over conventional techniques especially where real-time near-optimal solutions are required.

Artificial neural network–based internal leakage fault detection for hydraulic actuators: An experimental investigation

2017 ◽  
Vol 232 (4) ◽  
pp. 369-382 ◽  
Author(s):  
Zhikai Yao ◽  
Yongping Yu ◽  
Jianyong Yao
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Experimental Investigation ◽  
Signal Processing ◽  
Deep Neural Network ◽  
Detection Method ◽  
The Other ◽  
Detection Accuracy ◽  
Artificial Neural

Internal leakage is a typical fault in the hydraulic systems, which may be caused by seal damage, and result in deteriorated performance of the system. To study this issue, this article carries out an experimental investigation of artificial neural network–based detection method for internal leakage fault. A period of pressure signal at one chamber of the actuator was taken in response to sinusoidal-like inputs for the closed-loop controlled system as a basic signal unit, and totally, 1000 periodic signal units are obtained from the experiments. The above experimental measurements are repetitively implemented with 11 different active exerted internal leakage levels, that is, totally 11,000 basic signal units are obtained. For signal processing, the pressure signal in the operation condition without active exerted leakage is chosen to generate a baseline with suitable pre-proceed, and the relative values of the other basic signal units (D-value between the baseline and other original signals) act as the global samples of the following artificial neural networks, traditional back propagation neural network, deep neural network, convolution neural network and auto-encoder neural network, separately; 8800 samples by random extraction as train samples to train the above neural networks and the other samples different from the train samples act as test samples to examine the detection accuracy of the proposed method. It is shown that the deep neural network with five layers can obtain a best detection accuracy (92.23%) of the above-mentioned neural networks. In addition, the methods based on wavelet transform and Hilbert–Huang transform are also applied, and a comparison of these methods is provided at last. From the comparison, it is shown that the proposed detection method obtains a good result without a need to model the internal leakage or a complicated signal processing.

scholarly journals Early Detection of Photovoltaic Panel Degradation through Artificial Neural Network

2021 ◽  
Vol 11 (19) ◽  
pp. 8943
Author(s):  
Rudy Alexis Guejia Burbano ◽  
Giovanni Petrone ◽  
Patrizio Manganiello
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Environmental Conditions ◽  
A Priori ◽  
The Other ◽  
Photovoltaic Panel ◽  
Pv Panel ◽  
Artificial Neural ◽  
The Difference ◽  
Artificial Neural Network Ann

In this paper, an artificial neural network (ANN) is used for isolating faults and degradation phenomena occurring in photovoltaic (PV) panels. In the literature, it is well known that the values of the single diode model (SDM) associated to the PV source are strictly related to degradation phenomena and their variation is an indicator of panel degradation. On the other hand, the values of parameters that allow to identify the degraded conditions are not known a priori because they can be different from panel to panel and are strongly dependent on environmental conditions, PV technology and the manufacturing process. For these reasons, to correctly detect the presence of degradation, the effect of environmental conditions and fabrication processes must be properly filtered out. The approach proposed in this paper exploits the intrinsic capability of ANN to map in its architecture two effects: (1) the non-linear relations existing among the SDM parameters and the environmental conditions, and (2) the effect of the degradation phenomena on the I-V curves and, consequently, on the SDM parameters. The ANN architecture is composed of two stages that are trained separately: one for predicting the SDM parameters under the hypothesis of healthy operation and the other one for degraded condition. The variation of each parameter, calculated as the difference of the output of the two ANN stages, will give a direct identification of the type of degradation that is occurring on the PV panel. The method was initially tested by using the experimental I-V curves provided by the NREL database, where the degradation was introduced artificially, later tested by using some degraded experimental I-V curves.

Improving the Accuracy of an Artificial Neural Network Using Multiple Differently Trained Networks

1992 ◽  
Vol 4 (5) ◽  
pp. 772-780 ◽  
Author(s):  
William G. Baxt
Keyword(s):  
Neural Network ◽  
Myocardial Infarction ◽  
Artificial Neural Network ◽  
False Alarm ◽  
False Alarm Rate ◽  
Detection Rate ◽  
Rate Sensitivity ◽  
The Other ◽  
Risk Patients ◽  
Artificial Neural

When either detection rate (sensitivity) or false alarm rate (specificity) is optimized in an artificial neural network trained to identify myocardial infarction, the increase in the accuracy of one is always done at the expense of the accuracy of the other. To overcome this loss, two networks that were separately trained on populations of patients with different likelihoods of myocardial infarction were used in concert. One network was trained on clinical pattern sets derived from patients who had a low likelihood of myocardial infarction, while the other was trained on pattern sets derived from patients with a high likelihood of myocardial infarction. Unknown patterns were analyzed by both networks. If the output generated by the network trained on the low risk patients was below an empirically set threshold, this output was chosen as the diagnostic output. If the output was above that threshold, the output of the network trained on the high risk patients was used as the diagnostic output. The dual network correctly identified 39 of the 40 patients who had sustained a myocardial infarction and 301 of 306 patients who did not have a myocardial infarction for a detection rate (sensitivity) and false alarm rate (1-specificity) of 97.50 and 1.63%, respectively. A parallel control experiment using a single network but identical training information correctly identified 39 of 40 patients who had sustained a myocardial infarction and 287 of 306 patients who had not sustained a myocardial infarction (p = 0.003).

scholarly journals Hybrid artificial neural network and structural equation modelling techniques: a survey

2021 ◽  
Author(s):  
A. S. Albahri ◽  
Alhamzah Alnoor ◽  
A. A. Zaidan ◽  
O. S. Albahri ◽  
Hamsa Hameed ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Structural Equation Modelling ◽  
Structural Equation ◽  
Healthcare Sector ◽  
Autistic Children ◽  
Equation Modelling ◽  
Wide Range ◽  
Artificial Neural ◽  
Future Work

AbstractTopical treatments with structural equation modelling (SEM) and an artificial neural network (ANN), including a wide range of concepts, benefits, challenges and anxieties, have emerged in various fields and are becoming increasingly important. Although SEM can determine relationships amongst unobserved constructs (i.e. independent, mediator, moderator, control and dependent variables), it is insufficient for providing non-compensatory relationships amongst constructs. In contrast with previous studies, a newly proposed methodology that involves a dual-stage analysis of SEM and ANN was performed to provide linear and non-compensatory relationships amongst constructs. Consequently, numerous distinct types of studies in diverse sectors have conducted hybrid SEM–ANN analysis. Accordingly, the current work supplements the academic literature with a systematic review that includes all major SEM–ANN techniques used in 11 industries published in the past 6 years. This study presents a state-of-the-art SEM–ANN classification taxonomy based on industries and compares the effort in various domains to that classification. To achieve this objective, we examined the Web of Science, ScienceDirect, Scopus and IEEE Xplore® databases to retrieve 239 articles from 2016 to 2021. The obtained articles were filtered on the basis of inclusion criteria, and 60 studies were selected and classified under 11 categories. This multi-field systematic study uncovered new research possibilities, motivations, challenges, limitations and recommendations that must be addressed for the synergistic integration of multidisciplinary studies. It contributed two points of potential future work resulting from the developed taxonomy. First, the importance of the determinants of play, musical and art therapy adoption amongst autistic children within the healthcare sector is the most important consideration for future investigations. In this context, the second potential future work can use SEM–ANN to determine the barriers to adopting sensing-enhanced therapy amongst autistic children to satisfy the recommendations provided by the healthcare sector. The analysis indicates that the manufacturing and technology sectors have conducted the most number of investigations, whereas the construction and small- and medium-sized enterprise sectors have conducted the least. This study will provide a helpful reference to academics and practitioners by providing guidance and insightful knowledge for future studies.

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