首页 | 本学科首页   官方微博 | 高级检索  
相似文献
 共查询到10条相似文献,搜索用时 140 毫秒
1.
The navigation problem involves how to reach a goal avoiding obstacles in dynamic environments. This problem can be faced considering reactions and sequences of actions. Classifier systems (CSs) have proven their ability of continuous learning, however, they have some problems in reactive systems. A modified CS, namely a reactive classifier system (RCS), is proposed to overcome those problems. Two special mechanisms are included in the RCS: the non-existence of internal cycles inside the CS (no internal cycles) and the fusion of environmental message with the messages posted to the message list in the previous instant (generation list through fusion). These mechanisms allow the learning of both reactions and sequences of actions. This learning process involves two main tasks: first, discriminate between rules and, second, the discovery of new rules to obtain a successful operation in dynamic environments. DiVerent experiments have been carried out using a mini-robot Khepera to find a generalized solution. The results show the ability of the system for continuous learning and adaptation to new situations.  相似文献   

2.
The emergence of eXtended Classifier Systems (XCS) raised the bar for Learning Classifier Systems by incorporating the accuracies of the rules in the LCS's traditional reinforcement mechanism. However, neither XCS nor its extensions take into account the nature of a classifier's experience of attending the action set. We introduce an experience–evaluation mechanism that, once added to the traditional XCS, would assigns to each member of the action set a success rate indicating how effectively the classifier has contributed to the correct responding of the system to the environment's queries. Application of the augmented system (called SRXCS) to several benchmark problems shows that the proposed mechanism enhances XCS' classification capability and its rate of convergence at the same time. Application results indicate that SRXCS performs notably better on both pattern association and pattern recognition tasks. The applicability and efficiency of the proposed mechanism is further demonstrated through solving a fairly complex path planning problem for an autonomous mobile robot in a dynamic environment.  相似文献   

3.
The cooperative learning systems (COLS) are an interesting way of research in Artificial Intelligence. This is because an intelligence form can emerge by interacting simple agents in these systems. In literature, we can find many learning techniques, which can be improved by combining them to a cooperative learning, this one can be considered as a special case of bagging. In particular, learning classifier systems (LCS) are adapted to cooperative learning systems because LCS manipulate rules and, hence, knowledge exchange between agents is facilitated. However, a COLS has to use a combination mechanism in order to aggregate information exchanged between agents, this combination mechanism must take in consideration the nature of information manipulated by the agents. In this paper we investigate a cooperative learning system based on the Evidential Classifier System, the cooperative system uses Dempster–Shafer theory as a support to make data fusion. This is due to the fact that the Evidential Classifier System is itself based on this theory. We present some ways to make cooperation by using this architecture and discuss the characteristics of such architecture by comparing the obtained results with those obtained by an individual approach.  相似文献   

4.
This study reports the use of a Genetic Algorithm (GA) to solve the Power System Restoration Planning Problem (PSRP). The solution to the PSRP is described by a series of operations or a plan to be used by the Power System operator immediately on the occurrence of a blackout in the electrical power supply. Our GA uses new initialization and crossover operators based on the electrical power network, which are able to generate and maintain the plans feasible along GA runs. This releases the Power Flow program, which represents the most computer demanding component, from computing the fitness function of unfeasible individuals. The method was designed for large transmission systems and results for three different electrical power networks are shown: IEEE 14-Bus, IEEE 30-Bus, and a large realistic system.  相似文献   

5.
This paper proposes a hybrid neuro-evolutive algorithm (NEA) that uses a compact indirect encoding scheme (IES) for representing its genotypes (a set of ten production rules of a Lindenmayer System with memory), moreover has the ability to reuse the genotypes and automatically build modular, hierarchical and recurrent neural networks. A genetic algorithm (GA) evolves a Lindenmayer System (L-System) that is used to design the neural network’s architecture. This basic neural codification confers scalability and search space reduction in relation to other methods. Furthermore, the system uses a parallel genome scan engine that increases both the implicit parallelism and convergence of the GA. The fitness function of the NEA rewards economical artificial neural networks (ANNs) that are easily implemented. The NEA was tested on five real-world classification datasets and three well-known datasets for time series forecasting (TSF). The results are statistically compared against established state-of-the-art algorithms and various forecasting methods (ADANN, ARIMA, UCM, and Forecast Pro). In most cases, our NEA outperformed the other methods, delivering the most accurate classification and time series forecasting with the least computational effort. These superior results are attributed to the improved effectiveness and efficiency of NEA in the decision-making process. The result is an optimized neural network architecture for solving classification problems and simulating dynamical systems.  相似文献   

6.
Classifier systems and genetic algorithms   总被引:28,自引:0,他引:28  
Classifier systems are massively parallel, message-passing, rule-based systems that learn through credit assignment (the bucket brigade algorithm) and rule discovery (the genetic algorithm). They typically operate in environments that exhibit one or more of the following characteristics: (1) perpetually novel events accompanied by large amounts of noisy or irrelevant data; (2) continual, often real-time, requirements for action; (3) implicitly or inexactly defined goals; and (4) sparse payoff or reinforcement obtainable only through long action sequences. Classifier systems are designed to absorb new information continuously from such environments, devising sets of competing hypotheses (expressed as rules) without disturbing significantly capabilities already acquired. This paper reviews the definition, theory, and extant applications of classifier systems, comparing them with other machine learning techniques, and closing with a discussion of advantages, problems, and possible extensions of classifier systems.  相似文献   

7.
In manufacturing industries, the quality of a product depends on the combined effect of multiple input variables working singly or together and therefore attention has been given on process capability indices to shift from single to multivariate domain. In case of multivariable domain the capability to incorporate uncertainties at the time of decision making becomes necessary. Fuzzy system is introduced to take care of this requirement. In this article the process parameters of soap manufacturing industries have been analyzed. The process capability is determined using Fuzzy Inference System rule editor based on a set of justified if then statements as applicable for the process. The data has been collected in linguistic form to derive its process capability, using a set of justified rules and the effect of each factor has been determined using Design of Experiments (DoE) and analysis of variance technique (ANOVA) for improving the soap quality from perspective of its softness. This article ventures to propose a new methodology by integrating Fuzzy with DoE providing better result followed by DoE and Fuzzy Inference system in isolation.  相似文献   

8.
The article is about a new Classifier System framework for classification tasks called BYP-CS (for BaYesian Predictive Classifier System). The proposed CS approach abandons the focus on high accuracy and addresses a well-posed Data Mining goal, namely, that of uncovering the low-uncertainty patterns of dependence that manifest often in the data. To attain this goal, BYP-CS uses a fair amount of probabilistic machinery, which brings its representation language closer to other related methods of interest in statistics and machine learning. On the practical side, the new algorithm is seen to yield stable learning of compact populations, and these still maintain a respectable amount of predictive power. Furthermore, the emerging rules self-organize in interesting ways, sometimes providing unexpected solutions to certain benchmark problems.  相似文献   

9.
基于遗传算法的混合蚁群算法   总被引:1,自引:0,他引:1       下载免费PDF全文
提出了一种新的求连续空间最优值的蚁群算法。结合遗传算法和蚁群算法各自的优点以及两种算法融合基础,提出了遗传算法融入到蚁群算法融合中的两种新策略,第一种策略是先利用遗传算法具有比较强的全局搜索能力,在大范围内寻找一组解,然后以此为基础,用蚁群算法快速寻找最优解X*best;另一种策略是利用遗传算法交叉操作产生蚁群算法中的新旅行路径,以此提高蚁群算法的全局搜索能力。用上述策略构造两个基于遗传算法的混合遗传算法。用测试函数Rosenbrock和测试函数Shubert验证了混合蚁群算法的正确性。  相似文献   

10.
Concurrence is an important research area in collaborative problem solving.This paper offers a formal definition for cooperative sequences in multi-agent systems,discusses the different categories of concurrent actions.and proposes some rules for situation revision and an algorithm used to generate resulting situations.An example is also given to show how to solve concurrent problems occurring in multi-agent systems.  相似文献   

设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号