The condition of the tool in a turning operation is monitored by using artificial neural network (ANN). The recursive Kalman filter algorithm is used for weight updation of the ANN. To monitor the status of the tool, ...
详细信息
The condition of the tool in a turning operation is monitored by using artificial neural network (ANN). The recursive Kalman filter algorithm is used for weight updation of the ANN. To monitor the status of the tool, tool wear patterns are collected. The patterns are transformed from n-dimensional feature space to a lower dimensional space (two dimensions). This is done by using two discriminant vectors phi(1) and phi(2). These discriminant vectors are found by optimal discriminant plane method. Thirty patterns are used for training the ANN. A comparison between the classification performances of the ANN trained without reducing the dimensions of the input patterns and with reduced dimensions of the input patterns is done. The ANN trained with transformed tool wear patterns gives better results in terms of improved classification performance in less iteration, when compared with the results of the ANN trained without transforming the dimensions of the input patterns to a lower dimension.
In this paper, the theory of artificial neural network with back-propagation algorithm (BPN) is presented, and the BPN model is used to predict the accumulated temperature for Northeast China, North China, and the Hua...
详细信息
In this paper, the theory of artificial neural network with back-propagation algorithm (BPN) is presented, and the BPN model is used to predict the accumulated temperature for Northeast China, North China, and the Huang-Huai-Hai Plain. A total of 235 records collected from 235 meteorology stations were fed into the BPN model for training and testing. The latitude, longitude and elevation of each station were used as input variables of BPN, and the accumulated temperature as output variable. Other key network parameters, such as learning rate, momentum, the number of hidden nodes, and the learning iterations, were optimized using a trial and error approach. The optimized BPN model was compared with the multiple linear regression (MLR) model. In summary, BPN model was generally more accurate than MLR model. This infers that artificial neural network models are more applicable than regression models when predicting accumulated temperature. (C) 2009 Elsevier Ltd. All rights reserved.
A significant portion of the Mazandaran Province in Iran is prone to landslides due to climatic conditions, excessive rain, geology, and geomorphologic characteristics. These landslides cause damage to property and po...
详细信息
A significant portion of the Mazandaran Province in Iran is prone to landslides due to climatic conditions, excessive rain, geology, and geomorphologic characteristics. These landslides cause damage to property and pose a threat to human lives. Numerous solutions have been proposed to assess landslide susceptibility over regions such as this one. This study proposes an indirect assessment strategy that shares in the advantages of quantitative and qualitative assessment methods. It employs a fuzzy inference system (FIS) to model expert knowledge, and an artificial neural network (ANN) to identify non-linear behavior and generalize historical data to the entire region. The results of the FIS are averaged with the intensity values of existing landslides, and then used as outputs to train the ANN. The input patterns include both physical landscape characteristics (criterion maps) and landslide inventory maps. The ANN is trained with a modified back-propagation algorithm. As part of this study, the strategy is implemented as a GIS extension using ArcGIS (R). This tool was used to create a four-domain landslide susceptibility map of the Mazandaran province. The overall accuracy of the LSM is estimated at 90.5%. (C) 2010 Elsevier Ltd. All rights reserved.
An artificial neural network （ANN） is used to model the middle atmosphere using a large number of TIMED/SABER limb sounding temperature profiles. A three-layer feed-forward network is chosen based on the back-propag...
详细信息
An artificial neural network （ANN） is used to model the middle atmosphere using a large number of TIMED/SABER limb sounding temperature profiles. A three-layer feed-forward network is chosen based on the back-propagation （BP） algorithm. Latitude, longitude, and height are chosen as the input vectors of the network while temperature is the output vector. The temperature observations during the period from 13 January through 16 March 2007, which are in the same satellite yaw, are taken as samples to train an ANN. Results suggest that the network has high quality for modeling spatial variations of temperature. Quantitative comparisons between the ANN outputs and those from the popular empirical NRLMSISE-00 model illustrate their generally consistent features and some specific differences. The NRLMSISE-00 model＇s zonal mean temperatures are too high by ～6 K-10 K near the stratopause, and the amplitude and phase of the planetary wave number 1 activity are different in some respects from the ANN simulations above 45-50 km, suggesting improvement is needed in the NRLMSISE-00 model for more accurate simulation near and above the stratopause.
In this study, the efficiency of different artificial neural networks (ANNs) in predicting the torsional strength of reinforced concrete (RC) beams is firstly explored. Experimental data of 76 rectangular RC beams fro...
详细信息
In this study, the efficiency of different artificial neural networks (ANNs) in predicting the torsional strength of reinforced concrete (RC) beams is firstly explored. Experimental data of 76 rectangular RC beams from an existing database in the literature were used to develop ANN model. The input parameters affecting the torsional strength were selected as cross-sectional area of beams, dimensions of closed stirrups, spacing of stirrups, cross-sectional area of one-leg of closed stirrup, yield strength of stirrup and longitudinal reinforcement, steel ratio of stirrups, steel ratio of longitudinal reinforcement and concrete compressive strength. Each parameter was arranged in an input vector and a corresponding output vector that includes the torsional strength of RC beam. For all outputs, the ANN models were trained and tested using three layered 11 back-propagation methods. The initial performance evaluation of 11 different backpropagations was compared with each other. In addition to these, the paper presents a short review of the well-known building codes provisions for the design of RC beams under pure torsion. The accuracy of the codes in predicting the torsional strength of RC beams was also examined with comparable way by using same test data. The study shows that the ANN models give reasonable predictions of the ultimate torsional strength of RC beams (R-2 approximate to 0.988). Moreover, the study concludes that all ANN models predict the torsional strength of RC beams better than existing building code equations for torsion. (C) 2010 Elsevier Ltd. All rights reserved.
For the flexural reinforcement of bridge and building structure, synthetic materials whose dynamic properties are superior and those containing the merit of corrosion-proof are widely used as the substitute for a stee...
详细信息
For the flexural reinforcement of bridge and building structure, synthetic materials whose dynamic properties are superior and those containing the merit of corrosion-proof are widely used as the substitute for a steel plate. Since FRP plate has improved bond strength owing to the fibers externally adhering to the plate, many researches regarding the bond strength improvement have been substantially performed. To search out such bond strength improvement, previous researchers had ever examined the bond strength of FRP plate through their experiment by setting up many variables. However, since the experiment for a research on the bond strength takes much of expenditure for setting up the equipment and is time-consuming, also is difficult to be carried out, it is limitedly conducted. The purpose of this study was to develop the most suitable artificial neural network model by application of various neural network models and algorithm to the data of the bond strength experiment conducted by previous researchers. Many variables were used as input layers against bond strength: depth, width, modulus of elasticity, tensile strength of FRP plate and the compressive strength, tensile strength, and width of concrete. The developed artificial neural network model has been applied back-propagation, and its error was learned to be converged within the range of 0.001. Besides, the process for the over-fitting problem has been dissolved by Bayesian technique. The verification on the developed model was executed by comparison with the test results of bond strength made by other previous researchers, which was never been utilized to the learning as yet. (c) 2006 Wiley Periodicals, Inc.
A method based on ant colony algorithm (ACA) is proposed to train weights and thresholds for back-propagation (BP) neural network. BP algorithm has been widely used in training artificial neural network (ANN). This al...
详细信息
ISBN:
(纸本)9780769539829
A method based on ant colony algorithm (ACA) is proposed to train weights and thresholds for back-propagation (BP) neural network. BP algorithm has been widely used in training artificial neural network (ANN). This algorithm has many attractive features, such as adaptive learning, self-organism, and fault tolerant ability. All of them make BP one of the most successful algorithms in various fields. But, BP suffers from relatively slow convergence speed, extensive computations and possible divergence for certain conditions. As a new bionic algorithm, ACA has gained very good performance in solving traveling salesman problem (TSP) and other optimization problems. Its properties such as distributed computation, heuristic searching and robustness have well conquered the long convergence speed and premature problem, which are the main deficiencies of BP algorithm. Experiments suggest the method proposed has resolved those problems efficiently.
In this paper, radial basis function network (RBFN) with sliding-mode controller (SMC) is designed to the joint position control of two-link robot manipulators for periodic motion and predefined trajectory tracking co...
详细信息
ISBN:
(纸本)9781424465880
In this paper, radial basis function network (RBFN) with sliding-mode controller (SMC) is designed to the joint position control of two-link robot manipulators for periodic motion and predefined trajectory tracking control. Radial basis function uses curve fitting mode to obtain the nonlinear mapping. The unavoidable learning procedure degrades its transient performance in the existence of disturbance. Sliding-mode control is effective in overcoming uncertainties and has a fast transient response, while the control effort is discontinuous and creates chattering. For this defect, a saturation function is utilized to improve it. The back-propagation (BP) algorithm and Lyapunov stability theorem are used to decide a suitable update law and sliding-mode switch gain, respectively. Thus, the satisfied performance will be obtained, which better than the controller with single RBFN controller, sliding mode controller. The simulated results of a two-link robotic manipulator for the joint frictions, changing link masses and adding external disturbances are provided to show that the effectiveness of the proposed control scheme.
This paper proposes NeuDetect, which applies a classification rule mining Neural Network technique to wireless network packets captured through hardware sensors for purposes of real time detection of anomalous packets...
详细信息
ISBN:
(纸本)9781605589008
This paper proposes NeuDetect, which applies a classification rule mining Neural Network technique to wireless network packets captured through hardware sensors for purposes of real time detection of anomalous packets. To address the problem of high false alarm rate confronted by current wireless intrusion detection systems, this paper presents a method of applying artificial neural networks mining classification technique to wireless network intrusion detection system. The proposed system, NeuDetect, solution approach is to find normal and anomalous patterns on pre-processed wireless packet records by comparing them with training data using back-propagation algorithm.
This paper presents a power control strategy based on multi-resonant operating points, which is realized by multilayer feedforward neural network applying back-propagation algorithm. The full-bridge contactless power ...
详细信息
ISBN:
(纸本)9781424467129
This paper presents a power control strategy based on multi-resonant operating points, which is realized by multilayer feedforward neural network applying back-propagation algorithm. The full-bridge contactless power transfer system and magnetizing current of the transmitter on primary side are respectively used as research object and control variable. After batch-learning and training, the converged network determines alternating operating duty cycle of each resonant operating point in one cycle. By controlling magnetizing current, it can fulfill the dynamic regulation of transmission power, and increase the energy transfer efficiency. Simulation results show that in the control strategy, magnetizing current on the primary side can be stabilized at any set value for given range, and system has some disturbance restraint performance, satisfied with contactless power transfer system control demands.
暂无评论