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31.
The use of fuzzy logic and neural networks models for sensory properties prediction from process and structure parameters of knitted fabrics 总被引:1,自引:1,他引:0
Selsabil?El-Ghezal JeguirimEmail author Amal?Babay?Dhouib Mahdi?Sahnoun Morched?Cheikhrouhou Laurence?Schacher Dominique?Adolphe 《Journal of Intelligent Manufacturing》2011,22(6):873-884
In a competitive business environment, the textile industrialists intend to propose diversified products according to consumers
preference. For this purpose, the integration of sensory attributes in the process parameters choice seems to be a useful
alternative. This paper provides fuzzy and neural models for the prediction of sensory properties from production parameters
of knitted fabrics. The prediction accuracy of these models was evaluated using both the root mean square error (RMSE) and
mean relative percent error (MRPE). The results revealed the models ability to predict tactile sensory attributes based on
the production parameters. The comparison of the prediction performances showed that the neural models are slightly powerful
than the fuzzy models. 相似文献
32.
Mahdi Aliyari Shoorehdeli Mohammad Teshnehlab Ali Khaki Sedigh 《Neural computing & applications》2009,18(2):157-174
This paper suggests novel hybrid learning algorithm with stable learning laws for adaptive network based fuzzy inference system
(ANFIS) as a system identifier and studies the stability of this algorithm. The new hybrid learning algorithm is based on
particle swarm optimization (PSO) for training the antecedent part and gradient descent (GD) for training the conclusion part.
Lyapunov stability theory is used to study the stability of the proposed algorithm. This paper, studies the stability of PSO
as an optimizer in training the identifier, for the first time. Stable learning algorithms for the antecedent and consequent
parts of fuzzy rules are proposed. Some constraints are obtained and simulation results are given to validate the results.
It is shown that instability will not occur for the leaning rate and PSO factors in the presence of constraints. The learning
rate can be calculated on-line and will provide an adaptive learning rate for the ANFIS structure. This new learning scheme
employs adaptive learning rate that is determined by input–output data. 相似文献
33.
34.
Maryam Amiri Hassan Bakhshandeh Amnieh Mahdi Hasanipanah Leyli Mohammad Khanli 《Engineering with Computers》2016,32(4):631-644
Blasting operation is widely used method for rock excavation in mining and civil works. Ground vibration and air-overpressure (AOp) are two of the most detrimental effects induced by blasting. So, evaluation and prediction of ground vibration and AOp are essential. This paper presents a new combination of artificial neural network (ANN) and K-nearest neighbors (KNN) models to predict blast-induced ground vibration and AOp. Here, this combination is abbreviated using ANN-KNN. To indicate performance of the ANN-KNN model in predicting ground vibration and AOp, a pre-developed ANN as well as two empirical equations, presented by United States Bureau of Mines (USBM), were developed. To construct the mentioned models, maximum charge per delay (MC) and distance between blast face and monitoring station (D) were set as input parameters, whereas AOp and peak particle velocity (PPV), as a vibration index, were considered as output parameters. A database consisting of 75 datasets, obtained from the Shur river dam, Iran, was utilized to develop the mentioned models. In terms of using three performance indices, namely coefficient correlation (R 2), root mean square error and variance account for, the superiority of the ANN-KNN model was proved in comparison with the ANN and USBM equations. 相似文献
35.
Many environments and scenarios contain rough and irregular terrain and are inaccessible or hazardous for humans. Robotic automation is preferred in lieu of placing humans at risk. Legged locomotion is more advantageous in traversing complex terrain but requires constant monitoring and correction to maintain system stability. This paper presents a multi-legged reactive stability control method for maintaining system stability under external perturbations. Assuming tumbling instability and sufficient friction to prevent slippage, the reactive stability control method is based solely on the measured foot forces normal to the contact surface, reducing computation time and sensor information. Under external perturbations, the reactive stability control method opts to either displace the CG or the foot contacts of the robot based on the measured foot force distribution. Details describing the reactive stability control method are discussed including algorithms and an implementation example. An experimental demonstration of the reactive stability control method is presented. The experiment was conducted on a hexapod robot platform retrofitted with a tiny computer and force sensitive resistors to measure the foot forces. The experimental results show that the presented reactive stability control strategy prevents the robot from tipping over under external perturbation. 相似文献
36.
Multiple kernel learning (MKL) approach has been proposed for kernel methods and has shown high performance for solving some real-world applications. It consists on learning the optimal kernel from one layer of multiple predefined kernels. Unfortunately, this approach is not rich enough to solve relatively complex problems. With the emergence and the success of the deep learning concept, multilayer of multiple kernel learning (MLMKL) methods were inspired by the idea of deep architecture. They are introduced in order to improve the conventional MKL methods. Such architectures tend to learn deep kernel machines by exploring the combinations of multiple kernels in a multilayer structure. However, existing MLMKL methods often have trouble with the optimization of the network for two or more layers. Additionally, they do not always outperform the simplest method of combining multiple kernels (i.e., MKL). In order to improve the effectiveness of MKL approaches, we introduce, in this paper, a novel backpropagation MLMKL framework. Specifically, we propose to optimize the network over an adaptive backpropagation algorithm. We use the gradient ascent method instead of dual objective function, or the estimation of the leave-one-out error. We test our proposed method through a large set of experiments on a variety of benchmark data sets. We have successfully optimized the system over many layers. Empirical results over an extensive set of experiments show that our algorithm achieves high performance compared to the traditional MKL approach and existing MLMKL methods. 相似文献
37.
Elnaz Bigdeli Mahdi Mohammadi Bijan Raahemi Stan Matwin 《Pattern Analysis & Applications》2017,20(1):183-199
Clustering, while systematically applied in anomaly detection, has a direct impact on the accuracy of the detection methods. Existing cluster-based anomaly detection methods are mainly based on spherical shape clustering. In this paper, we focus on arbitrary shape clustering methods to increase the accuracy of the anomaly detection. However, since the main drawback of arbitrary shape clustering is its high memory complexity, we propose to summarize clusters first. For this, we design an algorithm, called Summarization based on Gaussian Mixture Model (SGMM), to summarize clusters and represent them as Gaussian Mixture Models (GMMs). After GMMs are constructed, incoming new samples are presented to the GMMs, and their membership values are calculated, based on which the new samples are labeled as “normal” or “anomaly.” Additionally, to address the issue of noise in the data, instead of labeling samples individually, they are clustered first, and then each cluster is labeled collectively. For this, we present a new approach, called Collective Probabilistic Anomaly Detection (CPAD), in which, the distance of the incoming new samples and the existing SGMMs is calculated, and then the new cluster is labeled the same as of the closest cluster. To measure the distance of two GMM-based clusters, we propose a modified version of the Kullback–Libner measure. We run several experiments to evaluate the performances of the proposed SGMM and CPAD methods and compare them against some of the well-known algorithms including ABACUS, local outlier factor (LOF), and one-class support vector machine (SVM). The performance of SGMM is compared with ABACUS using Dunn and DB metrics, and the results indicate that the SGMM performs superior in terms of summarizing clusters. Moreover, the proposed CPAD method is compared with the LOF and one-class SVM considering the performance criteria of (a) false alarm rate, (b) detection rate, and (c) memory efficiency. The experimental results show that the CPAD method is noise resilient, memory efficient, and its accuracy is higher than the other methods. 相似文献
38.
The structural properties of networked control systems with both bandwidth limitations and delays are investigated. Sufficient conditions are given for controllability (stabilizability) and reconstructibility (detectability). Our results enhance previous works by capturing bandwidth limitations and delays simultaneously. The adopted modeling framework could be readily used in control and estimation methods, including optimal and predictive schemes. It also facilitates the use of scheduling optimization algorithms in conjunction with the control scheme presented. 相似文献
39.
Determining the Main Factors in Declining the Urmia Lake Level by Using System Dynamics Modeling 总被引:6,自引:2,他引:4
Urmia Lake in Iran is the second largest saline lake in the world. This ecosystem is the home for different species. Due to
various socio-economical and ecological criteria, Urmia Lake has important role in the Northwestern part of the country but
it has faced many problems in recent years. Because of droughts, overuse of surface water resources and dam constructions,
water level has decreased in such a way that one quarter of the lake has changed to saline area in the last 10 years. The
purpose of this research is to determine the main factors which reduce the lake’s water level. To this end, a simulation model,
based on system dynamics method, is developed for the Urmia Lake basin to estimate the lake’s level. After successful verification
of the model, results show that (among the proposed factors) changes in inflows due to the climate change and overuse of surface
water resources is the main factor for 65% of the effect, constructing four dams is responsible for 25% of the problem, and
less precipitation on lake has 10% effect on decreasing the lake’s level in the recent years. In the future, the model also
can be used by managers as a decision support system to find the effects of building new dams or other infrastructures. 相似文献
40.
Farzad Hashemzadeh Iraj Hassanzadeh Mahdi Tavakoli Ghasem Alizadeh 《Journal of Intelligent and Robotic Systems》2012,68(3-4):245-259
In this paper, we introduce a new adaptive controller design scheme for nonlinear telerobotic systems with varying time delays where the delays and their variation rates are unknown. The designed controller has the ability to synchronize the state behaviors of the local and the remote robots. In this paper, asymptotic stability in the presence of varying time delays is of interest. Using the proposed controller, asymptotic stability of the bilateral telerobotic system subject to any bounded yet unknown varying delay with a bounded yet unknown rate of change can be guaranteed. Besides the varying time delay, the proposed adaptive controller has the ability to adapt to the parameter variations in the local and the remote robots’ dynamics. It is shown that position and velocity errors between the local and the remote manipulators converge to the zero asymptotically, thus ensuring teleoperation transparency. Experimental and simulation results with a pair of PHANToM haptic devices and a pair of planar manipulators under varying time delays in the communication channel demonstrate the effectiveness of the proposed scheme. 相似文献