MLP Neural network

Open Access Article

1 - An Ensemble Classifier Method for Breast Cancer Detection Using Genetic Algorithm and Multistage Adjustment of Weights in the MLP Neural Network
Amin Rezaeipanah S. J. Mirabedini ali mobaraki

Today, with the increasing spread of science, the use of decision support systems can be of great help in the therapeutic policies of the Doctor. For this purpose, the use of artificial intelligence systems in predicting and diagnosing breast cancer, which is one of the More

Today, with the increasing spread of science, the use of decision support systems can be of great help in the therapeutic policies of the Doctor. For this purpose, the use of artificial intelligence systems in predicting and diagnosing breast cancer, which is one of the most common cancers among women, is being considered. In this study, the process of diagnosis of breast cancer is done by using multistage weights in the MLP neural network in two layers. In the first layer, the three classifiers are trained simultaneously on the learning set data. Upon completion of the training, the output of the classifier of the first layer is accumulated together with the learning set data in the new sets. This set is given as an input to the second layer superconductor, and the supra-class mapping maps between the outputs of each of the ordinary classifiers of the first layer with the actual output classes. The three-layer structure of the first layer, as well as the second-layer supraclavicle, is a MLP neural network that optimizes the weights, effective properties and the size of the hidden layer simultaneously using an innovative genetic algorithm. In order to evaluate the accuracy of the proposed model, the Wisconsin database is used, which was created by the FNA test. Experiment results on the WBCD dataset the accuracy is 98.72% for the proposed method, which is relative to GAANN, CAFS algorithms provide better performance. Manuscript profile

Open Access Article

2 - Design a method for removing the cuff of polygraphy system in lie detection testusing MLP neural network
Mohammad Amin Younessi Heravi Mahdi Azarnoosh

Psychophysiologic verity test is a topic which is used widely in the world. Polygraphy system is usedfor recording the Psychophysiologic signals of a person in these tests and a cuff is used for measuringrelative changes of the arterial blood pressure and recording the More

Psychophysiologic verity test is a topic which is used widely in the world. Polygraphy system is usedfor recording the Psychophysiologic signals of a person in these tests and a cuff is used for measuringrelative changes of the arterial blood pressure and recording the velocity and power of the heart pulsein polygraph system. But the problem is that using cuff in various tests brings undesirable conditionsfor the body. The aim of this paper is to present a method for removing cuff in a way that desiredinformation is resulted in another way. This goal is followed by using arterial photoplethysmograhy.In order to do that, a model is presented which uses arterial volume to estimate blood pressure changessignal. This model was identified with MLP neural network. The output of this model is comparedwith blood pressure changes signal in three levels of signal, feature and classification. Reliability ofthe model was evaluated by presenting appropriate assessment criterions. The results were gained withthe 9.8% relative error, 4.4% relative error power and 80% accuracy of lie recognition in signalsassessed for blood pressure changes.According to the findings, the new method introduced in thisstudy has a comparable accuracy of results to the results of former methods while offering a morecomfortable recording and less diagnostic costs. This new method can be suggested for use as a liedetecting system. Manuscript profile

Open Access Article

3 - Determination of Lateral load Capacity of Steel Shear Walls Based on Artificial Neural Network Models
Meisam Bayat Ali Delnavaz

Manuscript profile

Open Access Article

4 - Application of Artificial Neural Networks to Identify Customers Satisfied with Car After Sales Services
Alireza Fazlzadeh Mohammad Sadegh Zeinali Kermani

The purpose of the research was the development of a neural networks model to identify satisfied customers for car maintenance service marketing. The data were collected from the survey of ten car maintenance service providers in Iran. Multi-layer perceptron (MLP) neura More

The purpose of the research was the development of a neural networks model to identify satisfied customers for car maintenance service marketing. The data were collected from the survey of ten car maintenance service providers in Iran. Multi-layer perceptron (MLP) neural networks with hyperbolic tangent function trained by feed forward training algorithm were utilized to build the identification model. The result reveals that the identification accuracy of the test on the model is greater than that expected by chance. Through a set of available contribution weights, the general importance of each independent variable produced is revealed. This research confirms that the neural network model is useful in recognizing the existing patterns of customer’s data. The advantages of using the model are highlighted. Authors believe that the model is useful and suitable as an analyzing tool for car maintenance service on market strategy planning. Manuscript profile

Open Access Article

5 - Online adaptive neuro-fuzzy controller design to attenuate the seismic responses in a 20-story benchmark structure
Rasoul Sabetahd Seyed Arash Mousavi Ghasemi Ramin Vafaei Poursorkhabi Ardashir Mohammadzadeh Yousef Zandi

10.30495/civil.2023.698648

In the present research, design of a strong and online adaptive controller in the active cable control system is discussed to overcome the earthquake vibrations of multi-story buildings. Considering all variables as unknown, this study introduces a new type 2 adaptive n More

In the present research, design of a strong and online adaptive controller in the active cable control system is discussed to overcome the earthquake vibrations of multi-story buildings. Considering all variables as unknown, this study introduces a new type 2 adaptive neuro-fuzzy controller. Using the MLP neural network (multi-layer perceptrons), Jacobian and the structural system estimation are extracted. This estimated structural system model is implemented into the online controller system in the next step. Adaptive controllers are tuned using a post-propagation algorithm and Extended Kalman Filter and are thus able to control and tune the controllers and the cable system. In this method, a PID controller is also used, which increases the strength and stability of the adaptive neural-fuzzy controller system two against earthquake vibrations. The superiority of the proposed controller system over an online simple adaptive controller is also demonstrated. This controller is utilized as an implicit reference model. In this proposed method, Extended Kalman Filter is innovatively used to tune online controllers. In this research, the performance of both controllers is investigated under the far and near fault field pressures. Based on the numerical results, the adaptive neural-fuzzy controller performs about 21% better than the online simple adaptive controller in minimizing the seismic responses of the structure during an earthquake and reaching the control criteria when the parametric characteristics of the structure change. Manuscript profile

Open Access Article

6 - Adaptive Control of the 3-Story Benchmark Building Equipped with MR Damper using Fractional Order Robust Controller
Ommegolsoum Jafarzadeh Seyed Arash Mousavi Ghasemi seyyed Mehdi Zahraei Ardashir Mohammadzadeh Ramin Vafaei Poursorkhabi

10.30495/civil.2023.702326

The goal of the present research is to propose a novel adaptive fractional order PID (AFOPID) controller whose parameters are tuned online by five exclusive multilayer perceptron (MLP) neural networks using the extended Kalman filter (EKF). An MLP neural network that is More

The goal of the present research is to propose a novel adaptive fractional order PID (AFOPID) controller whose parameters are tuned online by five exclusive multilayer perceptron (MLP) neural networks using the extended Kalman filter (EKF). An MLP neural network that is trained using the Back Propagation (BP) error algorithm is considered to identify the structural system and estimate the plant. The Jacobian of the model estimated online is utilized to apply to the controller. Considering the adaptive interval type-2 fuzzy neural networks (IT2FNN) and this issue that the compensator is tunned by EKF and feedback error learning strategy (FEL), the stability and robustness of this controller are increased against the estimation error, seismic disturbances, and some unknown nonlinear functions. In order to validate, the performance of the proposed controller is investigated on a 3-story nonlinear benchmark building equipped with semi-active dampers under far and near field earthquakes. In order to evaluate the effectiveness of the proposed controller equipped with a compensator in reducing seismic responses, the evaluation indices were discussed and compared with previous studies. The numerical results represent the substantial efficiency of the proposed adaptive controller (AFOPID) over the previous controllers such that J2 in the Hachinohe and Northridge earthquakes enhanced by up to 35% and more than 40%, respectively. In general, all indices ( J3  to J6 ) have experienced a considerable enhancement using the proposed method. Manuscript profile

Open Access Article

7 - Presentation DEA - MLP Neural NetworkModel in Selecting the Optimal Portfolio: Reviewing the Information Content of Accounting Criteria, Value-Based Criteria and BSC Criteria
Hasan Fattahi Nafchi mehdi arabsalehi Majid Esmaelian

Logical investment decisions require attention to different factors and different criteria at the same time. This goal can be achieved using various methods and algorithms. The purpose of this study is to develop an optimal stock portfolio model using a combination of d More

Logical investment decisions require attention to different factors and different criteria at the same time. This goal can be achieved using various methods and algorithms. The purpose of this study is to develop an optimal stock portfolio model using a combination of data envelopment analysis methods, anomaly clustering algorithm and MLP neural networks.The statistical population of the research is the accepted companies in Tehran Stock Exchange during the period of 1386 to 1396. To create an optimal stock portfolio, all available criteria were grouped to reach the optimal stock portfolio.Then, the results were compared in different approaches based on the Sharp ratio. The results of the research indicate that using the combination of data envelopment analysis, anomaly clustering, MLP neural networks and accounting metrics in the provision of an optimal portfolio of stocks led to Increasing Sharp's ratio compared to other approaches (Risk and Efficiency, Value-Based, and Balanced Scorecard). In general, the simultaneous use of hybrid optimization techniques and comprehensive criteria derived from accounting reports can provide a more efficient basket of portfolios and more desirability for the investors. Manuscript profile

Open Access Article

8 - APPLICATION NEURAL NETWORK TO SOLVE ORDINARY DIFFERENTIAL EQUATIONS
Nouredin Parandin Somayeh Ezadi

Manuscript profile

List of Articles MLP Neural network

Sanad

Links

Related Centers

Technical Support

Official pages