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7th International Conference on Machine Learning and Cybernetics

作者： Sharifi, Arash Vosolipour, Asiyeh Sh, Mahdi Aliyari Teshnehlab, Mohammad Islamic Azad Univ Sci & Res Branch Dept Comp Tehran Iran KN Toosi Univ Technol Elect Engn Dept Tehran Iran

ISBN: (纸本)9781424420957

In this study, a new group method of data handling (GMDH) method, based on adaptive neurofuzzy inference system (ANFIS) structure, called ANFIS-GMDH and its application for diabetes mellitus forecasting is presented. Conventional neurofuzzy GMDH (NF-GMDH) uses radial basis network (RBF) as the partial descriptions. In this study the RBF partial descriptions are replaced with two input ANFIS structures and backpropagation algorithm is chosen for learning this network structure. The Prima Indians diabetes data set is used as training and testing sets which consist of 768 data whereby 268 of them are diagnosed with diabetes. The result of this study will provide solutions to the medical staff in determining whether someone is (he diabetes sufferer or not which is much easier rather than currently doing a blood test. The results show that the proposed method performs better than the other models such as multi layer perceptron (MLP), RBF and ANFIS structure.

关键词： hierarchical Takagi-Sugeno fuzzy system ANFIS structure backpropagation algorithm diabetic mellitus

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Continuous-Discrete Time Neural Network Observer for Nonlinear Dynamic Systems Application to Vehicle Systems 22

Continuous-Discrete Time Neural Network Observer for Nonline...

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22nd World Congress of the International Federation of Automatic Control (IFAC)

作者： Ghani, Hasan Abdl Laghmara, Hind Ali, Sofiane Ahmed Ainouz, Samia Gao, Xing Khemmar, Redouane Normandie Univ UNIROUEN ESIGELEC Laboratoire IRSEEM F-76800 St Etienne Du Rouvray France Normandie Univ INSA Rouen LITIS F-76800 St Etienne Du Rouvray France Univ Paris Saclay IBISC Evry Val dEssonne Univ Evry France

ISBN: (纸本)9781713872344

This paper proposes a novel continuous-discrete (sampled data) time neural network (NSNN) observer for nonlinear systems. It can therefore be applied to systems with a high degree of non-linearity with no prior knowledge of the system dynamics. The proposed observer is a three-layer feedforward neural network that has been intensively trained using the error backpropagation learning algorithm, which includes an e-modification term to ensure robustness of the observer. A structure of the output predictor with a corrective term is added in the structure of the NN observer to overcome the problem of discrete time measurement. Simulations using MATLAB and CarSim are illustrated to demonstrate the performance of the proposed state observer strategy to reconstruct the state variables and parameters of a vehicle system. Copyright (c) 2023 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： Sampled data neural network Nonlinear observer backpropagation algorithm Vehicle systems

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Adaptive predistortion for solid state power amplifier using multi-layer perceptron

Adaptive predistortion for solid state power amplifier using...

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IEEE Global Telecommunications Conference (GLOBECOM 01)

作者： Langlet, F Abdulkader, H Roviras, D Mallet, A Castanié, F ENSEEIHT TeSA F-31071 Toulouse France

ISBN: (纸本)0780372069

In this paper, we present two adaptive predistortions techniques for non-linear satellite amplifiers. The amplifier gives two kind of distortions: amplitude distortion (AM/AM conversion) and phase distortion (AM/PM conversion). We compare performances of those predistortions techniques applied to the same amplifier. Both predistortions are realized with multi-layer perceptron (MLP) neural networks (NN) associated with the backpropagation (BP) algorithm. The first adaptive predistortion technique identifies the inverse AM/AM conversion with one NN and the direct AM/PM conversion with another one. The second one realizes directly the predistorsion by training an input NN at the input of the amplifier. This NN contains one structure for the inversion of the AM/AM conversion and one for the AM/PM conversion.

关键词： solid state power amplifier predistortion neural network backpropagation algorithm

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RBF neural network based model as an optimal classifier for the classification of radar returns from the ionosphere

RBF neural network based model as an optimal classifier for ...

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IEEE International Conference on Industrial Technology

作者： Salankar, Suresh S. Patre, Balasaheb M. BD Coll Engn Dept Elect & Telecommun Sevagram 442102 India SGGS Inst Engn & Technol Dept Instrumentat Nanded 431606 Maharashtra India

ISBN: (纸本)9781424407255

Research into the problem of classification of radar returns from the ionosphere has been taken up as a challenging task for the neural networks (NNs). It appears from the literature review that for the Multi layer Perceptron (MLP) NN trained with backpropagation, reported average classification accuracy was about 96% on the test instances. This paper investigates and designs an optimal classifier using a Radial Basis Function (RBF) NN. Authors compare the performance of two NN configurations, namely a well-known MLP NN model and the proposed RBF NN model on the radar dataset collected from the published studies. It is shown that the proposed RBF NN, consistently, has 100% accuracy on "bad" instances and 99.1935% accuracy on "good" instances. The results show that the proposed RBF NN classifier clearly outperforms the MP NN one in various performance measures such as MSE, NMSE, correlation coefficients, area under the ROC curve and classification accuracy on the testing datasets even after attempting different data partitions.

关键词： classification Multi-layer Perceptron Neural Network backpropagation algorithm Radial Basis Function Neural Network Receiver Operating Characteristics

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Using Convolutional Neural Networks to discover cogntively validated features for Gender Classification 1

Using Convolutional Neural Networks to discover cogntively v...

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2014 International Conference on Soft Computing & Machine Intelligence (ISCMI 2014)

作者： Verma, Ankit Vig, Lovekesh Jawaharlal Nehru Univ Sch Computat & Integrat Sci New Delhi 110067 India

ISBN: (纸本)9781479942312

The human visual cortex is extremely adept at distinguishing between male and female faces, or performing "Gender Classification". While the subject of face detection and recognition has received a lot of focus, research into the features or cognitive processes that are useful for identifying gender have received relatively little attention. Researchers have attempted to extract hand crafted features like wavelet coefficients, histograms etc. on the basis of which to generate a model to classify the male and female faces. However, these models tend to compress the image into a vector and disregard the two dimensional spatial correlations between the pixels in an image. Additionally, these features have to hand crafted and may or may not be ideal for the classification at hand. Ideally, the system should be able to generate specific features from the input face image which would help in classification of male faces from female faces. In this paper and a Deep Convolution Neural Network (CNN) model is presented for gender classification. The features generated by the CNN appear to agree with known results from the cognitive science community indicating that these models may be closer to biological neuronal processes governing gender classification. The classification results are compared with different regularization techniques and other standard classifiers, and the CNN models yield higher accuracy than both svms and random forest classifiers.

关键词： convolutional neural network backpropagation algorithm gender classification horizontal flipping L2 Regularization

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Study on range interval distance of prestressed anchor bars using update backpropagation neural network

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Journal of Coal Science & Engineering(China) 2003年第2期9卷 35-39页

作者：吴顺川张友葩高永涛 School of Civil and Environmental Engineering

Taking the practical reinforced engineering of a reinforced soil retaining wall as an example, which located in Shandong Province and set on 104 national highway, the stress spread behaviors of the anchor bars in the preforced proceeding were tested. According to the test data, and by use of the update backpropagation (BP) algorithm neural network(NN), the test method and it’s mechanism were studied by the network, then the learning results show the mean square error(MSE) only at the 2 55% level, and the proof testing results show the MSE at 4 38% level (the main aim is to build a NN directly from the in situ test results (the learning phase)). Ipso facto, the learning and adjustment abilities of the NN permit us to develop the test data, subsequently, 36 test data were acquired from the NN. By use of the provide data, as well as the failure situation and carried loading capacity of the retaining wall, finally, the choice the reasonable range interval distance of prestress cement grouting anchor bars were carried out, and the result was 2 m×2 m.

关键词： prestressed anchor bar range interval distance NN backpropagation algorithm

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An Automatic Detection of Citrus Fruits and Leaves Diseases using CNN 2

An Automatic Detection of Citrus Fruits and Leaves Diseases ...

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2nd International Conference on Sustainable Computing and Smart Systems (ICSCSS)

作者： Sujil, Adlin Kesavan, V. Thiruppathy Gopi, R. Danalakshmi, D. Govindasamy, R. Bishop Heber Coll Dept Comp Applicat Trichy Tamil Nadu India Dhanalakshmi Srinivasan Engn Coll Dept Informat Technol Perambalur Tamil Nadu India Dhanalakshmi Srinivasan Engn Coll Dept Comp Sci & Engn Perambalur Tamil Nadu India Dhanalakshmi Srinivasan Engn Coll Dept Elect & Elect Engn Perambalur Tamil Nadu India

ISBN: (纸本)9798350391558;9798350379990

Citrus diseases are caused due to bacterial infections. Prolonged infection leads to the degradation of overall productivity. This can be avoided through early detection and identification. This is implemented using Convolutional Neural Networks (CNNs), which provide a novel solution to overcome the constraints. CNNs are better suited for image recognition tasks, which helps in accurate identification of the spread of the bacteria. This is implemented using the multi-stage process. The images are pre-processed and help to provide optimum results. The CNN architecture is trained from the hierarchical demonstration of the input images through adopting convolutional layers. The detection of citrus diseases is done using the training and testing phase. This helps in the particular disease detection task. After the training process, the CNN are used for the representation of real-time disease detection. The input images from the agriculture are captured by drones or various automated imaging devices. Various iterative processes are done to enhance the robustness of the system. Thus, the CNN helps in early identification of diseases which helps to adopt various preventive measures and remedies for infectious fruits and leaves. CNN can able to make automatic decisions about disease control strategies which helps to improve the crop yield. They also provide positive approach to disease management

关键词： Citrus fruits canker automatic detection convolutional neural network image processing backpropagation algorithm

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Performance Comparison of Linear Multiuser Detectors and Neural Network Detector for DS/CDMA Systems in AWGN 10

Performance Comparison of Linear Multiuser Detectors and Neu...

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International Conference on Computer Engineering & Systems (ICCES)

作者： Hassan, Hassan A. Essai, Mohamed H. Yahya, Ahmed Al Azhar Univ Dept Elect Engn Cairo Egypt

ISBN: (纸本)9781467399715

The most commonly used multiple access technique in wireless communication sphere is the direct sequence code division multiple access. The main drawback of this system is multiple access interference (MAI) caused by sharing a number of users the same channel. Multiuser Detection enhances the performance of DS-CDMA system by combating MAI. In this paper, the performance of the neural network detector is compared with the linear multiuser detectors includes decorrelating (Decor.) detector, and Minimum Mean Squared Error detector (MMSE). This neural network detects the user bits after the bank of matched filter in additive white Gaussian noise channel, with using spreading code of Gold sequence (GS) type. Where these detectors work in both synchronous and asynchronous transmission modes, in this paper its performance was investigated in synchronous AWGN channel. Simulation results show that the performance of linear multiuser detectors depends mainly on the number of active users. The neural network detector is superior to the linear multiuser detectors in the terms of bit error rate (BER) performance.

关键词： DS/CDMA Multiuser detection matched filter Decorrelating detector MMSE detector neural network backpropagation algorithm

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A New Adaptive Learning algorithm to train Feed-Forward Multi-layer Neural Networks, Applied on Function Approximation Problem 4

A New Adaptive Learning algorithm to train Feed-Forward Mult...

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4th IEEE International Conference on Robotic Computing (IEEE IRC)

作者： Ghorrati, Zahra Purdue Univ Dept Comp Informat Technol W Lafayette IN 47907 USA

ISBN: (纸本)9781728152370

Slow convergence and inverse hessian calculation respectively, are the major drawbacks of first-order and second-order learning algorithms. This paper presents a new efficient algorithm to train feed-forward Multi-Layered Perceptron (MLP) neural network that doesn't require explicit computation of the inverse Hessian matrix. Due to the use of mathematical adaptive learning rates in the purposed approach, the rating speed is improved significantly compared to the first-order algorithms. The proposed method is applied to some function approximation problems and compared with backpropagation and modified backpropagation.

关键词： Multilayered feed-forward neural network backpropagation algorithm function approximation

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Control of Quadrotors Using Neural Networks for Precise Landing Maneuvers 1

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International Conference on Artificial Intelligence and Evolutionary Computations in Engineering Systems (ICAIECES)

作者： Ananthakrishnan, U. S. Akshay, Nagarajan Manikutty, Gayathri Bhavani, Rao R. Amrita Univ Amritapuri Amrita Vishwa Vidyapeetham Amrita Sch Engn AMMACHI Labs Kollam Kerala India

ISBN: (数字)9789811031748

ISBN: (纸本)9789811031748;9789811031731

Aerial and ground robots have been widely used in tandem to overcome the limitations of the individual systems, such as short run time and limited field of view. Several strategies have been proposed for this collaboration and all of them involve periodic autonomous precision landing of the aerial vehicle on the ground robot for recharging. Intelligent control systems like neural networks lend themselves naturally to precision landing applications since they offer immunity to system dynamics and adaptability to various environments. Our work describes an offline neural network backpropagation controller to provide visual servoing for the landing operation. The quadrotor control system is designed to perform precise landing on a marker platform within the specified time and distance constraints. The proposed method has been simulated and validated in a Gazebo and robot operating system simulation environment.

关键词： UAV-UGV collaboration Quadrotor landing Vision based navigation Neural networks backpropagation algorithm ROS architecture

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