Adaptive networks for physical modeling

This paper presents an original link between neural networks theory and mechanical modeling networks. The problem is to find the parameters characterizing mechanical structures in order to reproduce given mechanical behaviors. Replacing “neural” units with mechanically based units and applying classical learning algorithms dedicated to supervised dynamic networks to these mechanical networks allows us to find the parameters for a physical model. Some new variants of real-time recurrent learning (RTRL) are also introduced, based on mechanical principles.

The notion of interaction during learning is discussed at length and the results of tests are presented. Instead of the classical {machine learning system, environment} pair, we propose to study the {machine learning system, human operator, environment} triplet.

Experiments have been carried out in simulated scenarios and some original experiments with a force-feedback device are also described.     

Adaptive iterative learning control for robot manipulators

In this paper, we propose some adaptive iterative learning control (ILC) schemes for trajectory tracking of rigid robot manipulators, with unknown parameters, performing repetitive tasks. The proposed control schemes are based upon the use of a proportional-derivative (PD) feedback structure, for which an iterative term is added to cope with the unknown parameters and disturbances. The control design is very simple in the sense that the only requirement on the PD and learning gains is the positive definiteness condition and the bounds of the robot parameters are not needed. In contrast to classical ILC schemes where the number of iterative variables is generally equal to the number of control inputs, the second controller proposed in this paper uses just two iterative variables, which is an interesting fact from a practical point of view since it contributes considerably to memory space saving in real-time implementations. We also show that it is possible to use a single iterative variable in the control scheme if some bounds of the system parameters are known. Furthermore, the resetting condition is relaxed to a certain extent for a certain class of reference trajectories. Finally, simulation results are provided to illustrate the effectiveness of the proposed controllers.     

Event rates and aggregation in hierarchical discrete event systems

A discrete event system (DES) is a dynamical system whose evolution in time develops as the result of the occurrence of physical events at possibly irregular time intervals. Although many DES's operation is asynchronous, others have dynamics which depend on a clock or some other complex timing schedule. Here we provide a formal representation of the advancement of time for logical DES via interpretations of time. We show that the interpretations of time along with a timing structure provide a framework to study principles of the advancement of time for hierarchical DES (HDES). In particular, it is shown that for a wide class of HDES the event rate is higher for DES at the lower levels of the hierarchy than at the higher levels of the hierarchy. Relationships between event rate and event aggregation are shown. We define a measure for event aggregation and show that there exists an inverse relationship between the amount of event aggregation and the event rate at any two successive levels in a class of HDES. Next, we study how to design the timing structure to ensure that there will be a decrease in the event rate (by some constant factor) between any two levels of a wide class of HDES. It is shown that if the communications between the various DES in the HDES satisfy a certain admissibility condition then there will be a decrease in the event rate. These results for HDES constitute the main results of this paper, since they provide the first mathematical characterization of the relationship between event aggregation and event rates of the HDES and show how to design the interconnections in a HDES to achieve event rate reduction. Several examples are provided to illustrate the results.The authors gratefully acknowledge the partial support of the Jet Propulsion Laboratory. Please address all correspondence to K. Passino (email: passino@eagle.eng.ohio-state.edu).     

机器学习中自适应k值的k均值算法改进

针对k-means算法对于远离群点敏感和k值难以确定等缺陷,在分析已有的k-means改进算法的基础上,引进肘部法则的思想对数据进行优化处理并且根据自适应思想结合误差平方和SSE(sum of squared error),提出一种自适应调整k值的k-means改进算法。选取机器学习库中的真实数据集进行仿真实验,其结果表明,改进后的k-means算法中的剔除远离群点和自适应调整k值的方法均可行,准确性高、聚类效果质量更优。     

Adaptive personalized recommendation based on adaptive learning

Collaborative filtering has been widely applied in many fields in recent years due to the increase in web-based activities such as e-commerce and online content distribution. Current collaborative filtering techniques such as correlation-based, SVD-based and supervised learning-based approaches provide good accuracy, but are computationally very expensive and can only be deployed in static off-line settings, where the known rating information does not change with time. However, a number of practical scenarios require dynamic adaptive collaborative filtering that can allow new users, items and ratings to enter the system at a rapid rate. In this paper, we consider a novel adaptive personalized recommendation based on adaptive learning. Fast adaptive learning runs through all the aspects of the proposed approach, including training, prediction and updating. Empirical evaluation of our approach on Movielens dataset demonstrates that it is possible to obtain accuracy comparable to that of the correlation-based, SVD-based and supervised learning-based approaches at a much lower computational cost.     

Adaptive learning algorithm of self-organizing teams

In order to improve the ability of achieving good performance in self-organizing teams, this paper presents a self-adaptive learning algorithm for team members. Members of the self-organizing teams are simulated by agents. In the virtual self-organizing team, agents adapt their knowledge according to cooperative principles. The self-adaptive learning algorithm is approached to learn from other agents with minimal costs and improve the performance of the self-organizing team. In the algorithm, agents learn how to behave (choose different game strategies) and how much to think about how to behave (choose the learning radius). The virtual team is self-adaptively improved according to the strategies’ ability of generating better quality solutions in the past generations. Six basic experiments are manipulated to prove the validity of the adaptive learning algorithm. It is found that the adaptive learning algorithm often causes agents to converge to optimal actions, based on agents’ continually updated cognitive maps of how actions influence the performance of the virtual self-organizing team. This paper considered the influence of relationships in self-organizing teams over existing works. It is illustrated that the adaptive learning algorithm is beneficial to both the development of self-organizing teams and the performance of the individual agent.     

Data spread-based entropy clustering method using adaptive learning

Clustering analysis is to identify inherent structures and discover useful information from large amount of data. However, the decision makers may suffer insufficient understanding the nature of the data and do not know how to set the optimal parameters for the clustering method. To overcome the drawback above, this paper proposes a new entropy clustering method using adaptive learning. The proposed method considers the data spreading to determine the adaptive threshold within parameters optimized by adaptive learning. Four datasets in UCI database are used as the experimental data to compare the accuracy of the proposed method with the listing clustering methods. The experimental results indicate that the proposed method is superior to the listing methods.     

Adaptive fuzzy inference neural network

An adaptive fuzzy inference neural network (AFINN) is proposed in this paper. It has self-construction ability, parameter estimation ability and rule extraction ability. The structure of AFINN is formed by the following four phases: (1) initial rule creation, (2) selection of important input elements, (3) identification of the network structure and (4) parameter estimation using LMS (least-mean square) algorithm. When the number of input dimension is large, the conventional fuzzy systems often cannot handle the task correctly because the degree of each rule becomes too small. AFINN solves such a problem by modification of the learning and inference algorithm.     

Adaptive immunity based reinforcement learning

Recently much attention has been paid to intelligent systems which can adapt themselves to dynamic and/or unknown environments by the use of learning methods. However, traditional learning methods have a disadvantage that learning requires enormously long amounts of time with the degree of complexity of systems and environments to be considered. We thus propose a novel reinforcement learning method based on adaptive immunity. Our proposed method can provide a near-optimal solution with less learning time by self-learning using the concept of adaptive immunity. The validity of our method is demonstrated through some simulations with Sutton’s maze problem. This work was present in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

基于正交最小二乘和UD分解的模糊建模方法

通过改进模糊聚类方法确定模糊模型的前件结构,并对模糊推理关系矩阵进行正交最小二乘估计。通过分析正交向量在模型中贡献的大小确定聚类规则的有效性,然后采用基于UD分解的最小二乘确定模糊模型的后件参数,实现模糊模型的结构和参数的优化。该方法已成功地应用于Box-Jenkins煤气炉的数据系统建模。     

A fuzzy modeling method via Enhanced Objective Cluster Analysis for designing TSK model

This paper proposes a fuzzy modeling method via Enhanced Objective Cluster Analysis to obtain the compact and robust approximate TSK fuzzy model. In our approach, the Objective Cluster Analysis algorithm is introduced. In order to obtain more compact and more robust fuzzy rule prototypes, this algorithm is enhanced by introducing the Relative Dissimilarity Measure and the new consistency criterion to represent the similarity degree between the clusters. By these additional criteria, the redundant clusters caused by iterations are avoided; the subjective influence from human judgment for clustering is weakened. Moreover the clustering results including the number of clusters and the cluster centers are considered as the initial condition of the premise parameters identification. Thus the traditional iteration modeling procedure for determining the number of rules and identifying parameters is changed into one-off modeling, which significantly reduces the burden of computation. Furthermore the decomposition errors and the approximation errors resulted from premise parameters identification by Fuzzy c-Means clustering are decreased. For the consequence parameters identification, the Stable Kalman Filter algorithm is adopted. The performance of the proposed modeling method is evaluated by the example of Box–Jenkins gas furnace. The simulation results demonstrate the power of our model.     

Adaptive fuzzy identification and predictive control for industrial processes

This paper proposes a method for adaptive identification and control for industrial applications. The learning of a T–S fuzzy model is performed from input/output data to approximate unknown nonlinear processes by a hierarchical genetic algorithm (HGA). The HGA approach is composed by five hierarchical levels where the following parameters of the T–S fuzzy system are learned: input variables and their respective time delays, antecedent fuzzy sets, consequent parameters, and fuzzy rules. In order to reduce the computational cost and increase the algorithm’s performance an initialization method is applied on HGA. To deal with nonlinear plants and time-varying processes, the T–S fuzzy model is adapted online to maintain the quality of the identification/control. The identification methodology is proposed for two application problems: (1) the design of data-driven soft sensors, and (2) the learning of a model for the Generalized predictive control (GPC) algorithm. The integration of the proposed adaptive identification method with the GPC results in an effective adaptive predictive fuzzy control methodology. To validate and demonstrate the performance and effectiveness of the proposed methodologies, they are applied on identification of a model for the estimation of the flour concentration in the effluent of a real-world wastewater treatment system; and on control of a simulated continuous stirred tank reactor (CSTR) and on a real experimental setup composed of two coupled DC motors. The results are presented, showing that the developed evolving T–S fuzzy model can identify the nonlinear systems satisfactorily and it can be used successfully as a prediction model of the process for the GPC controller.     

Fuzzy-based Adaptive Routing Algorithm for Networks-on-Chip

In this paper, we propose two adaptive routing algorithms to alleviate congestion in the network. In the first algorithm, the routing decision is assisted by the number of occupied buffer slots at the corresponding input buffer of the next router and the congestion level of that router. Although this algorithm performs better than the conventional method, DyXY, in some cases the proposed algorithm leads to non-optimal decisions. Fuzzy controllers compensate for ambiguities in the data by giving a level of confidence rather than declaring the data simply true or false. To make a better routing decision, we propose an adaptive routing algorithm based on fuzzy logic for Networks-on-chip where the routing path is determined based on the current condition of the network. The proposed algorithm avoids congestion by distributing traffic over the routers that are less congested or have a spare capacity. The output of the fuzzy controller is the congestion level, so that at each router, the neighboring router with the lowest congestion value is chosen for routing a packet. To evaluate the proposed routing method, we use two multimedia applications and two synthetic traffic profiles. The experimental results show that the fuzzy-based routing scheme improves the performance over the DyXY routing algorithm by up to 25% with a negligible hardware overhead.     

应用自适应神经模糊推理系统(ANFIS)进行建模与仿真

模糊规划的提取和隶属度函数的学习是模糊推理系统设计中重要而困难的问题,自适应神经模糊推理系统（ANFIS）方法基于Sugeno模糊模型,其结构类似于神经网络,采用反向传播算法和最小二乘法调整模糊推理系统的参数,并能自动产生模糊规划,本文应用该方法给出了对一个典型系统建模的仿真实例,取得了良好的效果。     

自适应入侵检测模型

本文研究的是一种新型的自适应入侵检测模型,它主要用于解决大多数系统自适应能力差的缺点。在该模型中采用了模糊综合评判技术,同时在数据源头就开始检测数据包,从而将异常的网络包过滤掉。研究结果表明该检测模型能够自动的识别不断出现的新攻击行为,并且大大提高了检测效率。     

一类高阶非线性参数化系统自适应重复学习控制

针对一类高阶非线性参数化系统,利用参数重组技巧,提出了一种自适应重复学习控制方法.该方法结合反馈线性化,可以处理参数在一个未知紧集内周期性、快时变的非线性系统.通过引进微分-差分参数自适应律,设计了一种自适应控制策略,使广义跟踪误差在误差平方范数意义下渐近收敛于零.通过构造Lyapunov泛函,给出了闭环系统收敛的一个充分条件.实例仿真结果说明了该方法的可行性和有效性.     

基于自组织神经网络的非线性系统建模

针对基于T-S模糊模型的非线性系统建模问题,提出了一种基于自组织神经网络的新方法.在T-S模糊模型的建模中,目前常用的模糊C均值聚类算法存在迭代次数多,计算耗时的缺点.首先,利用竞争学习算法对输入空间进行聚类,基于此结果,借助于模糊C均值聚类算法进一步优化聚类结果,提取T-S模糊模型的规则前件隶属函数参数.然后,采用最小二乘法求得T-S模糊模型的规则后件参数,从而建立起非线性系统的T-S模糊模型.最后,仿真结果表明,该方法可以为模糊建模提供好的模型结构,并且有较高的计算效率和精度.     

Fuzzy assisted learning in backpropagation neural networks

This paper reports on studies to overcome difficulties associated with setting the learning rates of backpropagation neural networks by using fuzzy logic. Building on previous research, a fuzzy control system is designed which is capable of dynamically adjusting the individual learning rates of both hidden and output neurons, and the momentum term within a back-propagation network. Results show that the fuzzy controller not only eliminates the effort of configuring a global learning rate, but also increases the rate of convergence in comparison with a conventional backpropagation network. Comparative studies are presented for a number of different network configurations. The paper also presents a brief overview of fuzzy logic and backpropagation learning, highlighting how the two paradigms can enhance each other.