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51.
One of the most important assumptions in production scheduling is that the machines are permanently available without any breakdown. In the real world of scheduling, machines can be made unavailable due to various reasons such as preventive maintenance and unpredicted breakdown. In this paper, we explore flowshop configuration under the assumption of condition-based maintenance to minimize expected makespan. Furthermore, we consider a condition-based maintenance (CBM) strategy which could be used in most industrial settings. The proposed algorithm is designed for non-resumable flowshop state where the processing of jobs after preventive maintenance is restarted from the beginning. We propose a hybrid algorithm based on genetic algorithm and simulated annealing. Additionally, we conduct an extensive parameter calibration with the utilization of Taguchi method and select the optimal levels of the algorithm’s performance influential factors. The preliminary results indicate that the proposed method provides significantly better results compared with other high performing algorithms in the literature. 相似文献
52.
Stochastic chaos synchronization using Unscented Kalman-Bucy Filter and sliding mode control 总被引:1,自引:0,他引:1
Mahdi HeydariHassan Salarieh Mehdi Behzad 《Mathematics and computers in simulation》2011,81(9):1770-1784
This paper presents an algorithm for synchronizing two different chaotic systems by using a combination of Unscented Kalman-Bucy Filter (UKBF) and sliding mode controller. It is assumed that the drive chaotic system is perturbed by white noise and shows stochastic chaotic behavior. In addition the output of the system does not contain the whole state variables of the system, and it is also affected by some independent white noise. By combining the UKBF and the sliding mode control, a synchronizing control law is proposed. Simulation results show the ability of the proposed method in synchronizing chaotic systems in presence of noise. 相似文献
53.
Mahdi JaliliAuthor Vitae 《Neurocomputing》2011,74(10):1551-1556
Although diffusive electrical connections in neuronal networks are instantaneous, excitatory/inhibitory couplings via chemical synapses encompass a transmission time-delay. In this paper neural networks with instantaneous electrical couplings and time-delayed excitatory/inhibitory chemical connections are considered and scaling of the spike phase synchronization with the unified time-delay in the network is investigated. The findings revealed that in both excitatory and inhibitory chemical connections, the phase synchronization could be enhanced by introducing time-delay. The role of the variability of the neuronal external current in the phase synchronization is also investigated. As individual neuron models, Hindmarsh-Rose model is adopted and the network structure of the electrical and chemical connections is considered to be Watts-Strogatz and directed random networks, respectively. 相似文献
54.
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
55.
A resource investment problem with discounted cash flows (RIPDCF) is a project-scheduling problem in which (a) the availability levels of the resources are considered decision variables and (b) the goal is to find a schedule such that the net present value of the project cash flows optimizes. In this paper, the RIPDCF in which the activities are subject to generalized precedence relations is first modeled. Then, a genetic algorithm (GA) is proposed to solve this model. In addition, design of experiments and response surface methodology are employed to both tune the GA parameters and to evaluate the performance of the proposed method in 240 test problems. The results of the performance analysis show that the efficiency of the proposed GA method is relatively well. 相似文献
56.
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
57.
58.
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
59.
Seyedali Mirjalili Seyed Mohammad Mirjalili Abdolreza Hatamlou 《Neural computing & applications》2016,27(2):495-513
This paper proposes a novel nature-inspired algorithm called Multi-Verse Optimizer (MVO). The main inspirations of this algorithm are based on three concepts in cosmology: white hole, black hole, and wormhole. The mathematical models of these three concepts are developed to perform exploration, exploitation, and local search, respectively. The MVO algorithm is first benchmarked on 19 challenging test problems. It is then applied to five real engineering problems to further confirm its performance. To validate the results, MVO is compared with four well-known algorithms: Grey Wolf Optimizer, Particle Swarm Optimization, Genetic Algorithm, and Gravitational Search Algorithm. The results prove that the proposed algorithm is able to provide very competitive results and outperforms the best algorithms in the literature on the majority of the test beds. The results of the real case studies also demonstrate the potential of MVO in solving real problems with unknown search spaces. Note that the source codes of the proposed MVO algorithm are publicly available at http://www.alimirjalili.com/MVO.html. 相似文献
60.
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