38422944
Neural-Network-Based Fuzzy Logic Control and Decision System
A general neural-network (connectionist) model for fuzzy logic control and decision systems is proposed. This connectionist model, in the form of feedforward multilayer net, combines the idea of fuzzy logic controller and neural-network structure and learning abilities into an integrated neural-network-based fuzzy logic control and decision system. A fuzzy logic control decision network is constructed automatically by learning the training examples itself. By combining both unsupervised (self-organized) and supervised learning schemes, the learning speed converges much faster than the original backpropagation learning algorithm. The connectionist structure avoids the rule-matching time of the inference engine in the traditional fuzzy logic system. Two examples are presented to illustrate the performance and applicability of the proposed model. >
33340433
An evolutionary approach to the job-shop scheduling problem
This paper focuses on the heuristic hybridization and the genetic search as a methodology to develop a computationally efficient heuristic for the job-shop scheduling problem (JSP). In order to adapt the JSP to a genetic algorithm (GA), the ASGPL (Active-Schedule Generation with a Priority-List) algorithm with a hopping scheme was proposed, and using a GA, an iterative schedule improvement procedure called EVIS (Evolutionary Intracell Scheduler) was designed. The genetic search in EVIS was parallelized with a model of subpopulations and migration. Without implementing any problem-tailored heuristic for the job-shop scheduling problem, EVIS was able to find optimal solutions to a number of different problem instances in reasonable computation time.<<ETX>>
30064492
Genetic reinforcement learning approach to the machine scheduling problem
This paper focuses on the development of a learning-based heuristic for the machine scheduling problem, which automatically captures the search control knowledge or the common features of good schedules while generating a number of schedules. Defining states and actions of the machine shop, the machine scheduling problem is transformed into a problem of reinforcement learning (RL) in which a learner or a scheduler will learn to select the right action at each state of the machine shop, using the reward from a schedule evaluator for executing the action. Implementing the proposed reinforcement learning with a genetic algorithm results in the genetic reinforcement learning (GRL) approach to the machine scheduling problem. Although the learning-based heuristic has the overhead of acquiring knowledge on the problem, it can be easily adapted for a wide variety of machine scheduling problems due to the weak dependence on the problem structures and objectives. A GRL-based scheduler, called EVIS (Evolutionary Intracell Scheduler), has been developed and applied to various classes of machine scheduling problems, such as the job-shop scheduling, the flow-shop scheduling and the open-shop scheduling problems, and even the processor scheduling problem, the performance evaluation of EVIS with a number of different problem instances has shown that the learning-based heuristic is robust and its performance is comparable with that of other problem-specific heuristics or search-oriented heuristics in the quality of solutions.
41633235
Automatic generation of error recovery knowledge through learned reactivity
Develops a new method for automatic generation of error recovery procedures in the assembly workcell. Current reactive systems have integrated planning and learning capabilities to allow them to augment their store of reactive behaviors whenever they are forced to plan. The cycle of evaluation and reaction used by these systems implicitly detects and corrects any errors made in prior behavior execution. By capitalizing on this analogy to error recovery, the authors are able to gain the benefits of a reactive system for use in error recovery while minimizing the perception and evaluation burden that is inherent in those approaches. The authors present a system that uses the new learning reactive paradigm to quickly correct known errors and autonomously learn to recover new errors without human intervention.<<ETX>>
12503943
Model-based visual feedback control for a hand-eye coordinated robotic system
The integration of a single camera into a robotic system to control the relative position and orientation between the robot's end-effector and a moving part in real time is discussed. Only monocular vision techniques are considered because of current limitations in the speed of computer vision analysis. The approach uses geometric models of both the part and the camera, as well as the extracted image features, to generate the appropriate robot control signals for tracking. Part and camera models are also used during the teaching stage to predict important image features that appear during task completion.<<ETX>>
1173830
A genetic-algorithm-based neighbor-selection strategy for hybrid peer-to-peer networks
BitTorrent is a popular, open-source, hybrid peer-to-peer content distribution system that is conducive for distribution of large-volume contents. In this paper, we propose a genetic-algorithm-based neighbor-selection strategy for hybrid peer-to-peer networks, which enhances the decision process performed at the tracker for transfer coordination. We also investigate how the strategy affects system throughput and distribution efficiency as well as peer contributions. We show through computer simulations that by increasing content availability to the clients from their immediate neighbors, it can significantly improve the system performance without trading off users' satisfaction. The proposed strategy can significantly improve the efficiency of distribution, especially for low-connectivity peers, and it is suitable to deploy for online decisions
34408109
Modeling GIS Data Using OMEGA
6076905
Finite-horizon scheduling of radar dwells with online template construction
Timing constraints for radar tasks are usually specified in terms of the minimum and maximum temporal distance between successive radar dwells. We utilize the idea of feasible intervals for dealing with the temporal distance constraints. In order to increase the freedom that the scheduler can offer a high-level resource manager, we introduce a technique for nesting and interleaving dwells online while accounting for the energy constraint that radar systems need to satisfy. Further, in radar systems, the task set changes frequently and we advocate the use of finite horizon scheduling in order to avoid the pessimism inherent in schedulers that assume a task will execute forever. The combination of feasible intervals and online dwell packing allows modular schedule updates whereby portions of a schedule can be altered without affecting the entire schedule, hence reducing the complexity of the scheduler. Through extensive simulations we validate our claims of providing greater scheduling flexibility without compromising on performance when compared with earlier work based on templates constructed offline. We also evaluate the impact of two parameters in our scheduling approach: the template length (or the extent of dwell nesting and interleaving) and the length of the finite horizon.
2900634
Radar dwell scheduling considering physical characteristics of phased array antenna
This paper proposes novel techniques for scheduling radar dwells in phased array radar systems. In order to handle complex physical characteristics such as dwell interleaving, transmitting duty cycle constraint, and energy constraint, we propose a notion of schedulability envelope. The schedulability envelope designed offline hides the details of complex radar dwell scheduling and provides a simple measure for the schedulability check. Using the schedulability envelope, the proposed technique can efficiently perform the admission control for dynamic target tracking tasks. The simulation results show that the proposed approach can significantly improve the system utilization by taking advantage of dwell interleaving while guaranteeing the schedulability and physical constraints.
9595362
Genetic-based fuzzy image filter and its application to image processing
In this paper, we propose a Genetic-based Fuzzy Image Filter (GFIF) to remove additive identical independent distribution (i.i.d.) impulse noise from highly corrupted images. The proposed filter consists of a fuzzy number construction process, a fuzz filtering process, a genetic learning process, and an image knowledge base. First, the fuzzy number construction process receives sample images or the noise-free image and then constructs an image knowledge base for the fuzzy filtering process. Second, the fuzzy filtering process contains a parallel fuzzy inference mechanism, a fuzzy mean process, and a fuzzy decision process to perform the task of noise removal. Finally, based on the genetic algorithm, the genetic learning process adjusts the parameters of the image knowledge base. By the experimental results, GFIF achieves a better performance than the state-of-the-art filters based on the criteria of Peak-Signal-to-Noise-Ratio (PSNR), Mean-Square-Error (MSE), and Mean-Absolute-Error (MAE). On the subjective evaluation of those filtered images, GFIF also results in a higher quality of global restoration.
33337266
A genetic fuzzy agent using ontology model for meeting scheduling system
A Genetic Fuzzy Agent (GFA) using the ontology model for Meeting Scheduling System (MSS) is presented in this paper. The ontology model includes the Fuzzy Meeting Scheduling Ontology (FMSO) and the Fuzzy Personal Ontology (FPO) that can support to construct the knowledge base of the GFA. The FMSO is utilized to record and describe the meeting scheduling domain knowledge for the GFA. In addition, we implement a FMSO editor for generating the Web Ontology Language, OWL, that will be utilized by the GFA. Furthermore, the GFA will infer the suitable meeting time slots based on the ontology model. Moreover, it also adjusts the FMSO and FPO based on the results of the genetic learning mechanism for the next meeting. The experimental results show that our approach can effectively work for MSS.