基于视觉原理和Weber定律的TSK模糊系统建模

提出了一种新的基于视觉原理和Weber定律的TSK模糊系统建模方法.该方法首先基于视觉原理和Weber定律定义了一种新的目标函数,接着将其应用于TSK模糊系统建模中的参数学习.它不但能从训练样本的整体分布结构来进行参数学习,还能充分利用人眼很强的目标识别能力,从而能有效建模.所做的大量仿真试验表明了本文方法的有效性.该工作的意义在于从一个新的角度来审视TSK模糊系统建模问题,并为之提出了一种新的方法.     

基于混合共享机制的多任务深度学习方法

针对多任务学习的特征提取和任务区分难题，提出基于混合共享机制的多任务深度学习方法。给出硬参数共享网络混合训练方法和依据灵敏性分析的任务相似度分组策略；对组内、组间任务分别应用硬、软参数共享，给出混合共享网络及其相应训练方法；通过MNIST数据集上的实例研究与分析验证该方法的有效性。该方法充分发挥了硬、软参数共享机制的优点，较好刻画了任务的共享与私有特征，提升了多任务学习的性能。     

结构辨识和参数优化协同学习的概率TSK模糊系统

传统Takagi-Sugeno-Kang (TSK)模糊系统的结构辨识和参数优化往往分阶段进行, 同时模糊规则数需要预先设定, 因此TSK模糊系统的逼近性能和解释性往往不理想.针对此问题, 提出了一种结构辨识和参数优化协同学习的概率TSK模糊系统(Probabilistic TSK fuzzy system, PTSK).首先, PTSK使用概率模型表示模糊回归系统, 将结构辨识和参数优化作为一个整体来考虑.其次, PTSK不借助于专家经验, 使用粒子滤波方法对规则数和前后件参数协同学习, 得到系统全部参数的最优解.实验结果表明, PTSK具有良好的逼近性能, 同时能获得较少的模糊规则数.     

基于层次聚类多任务学习的人类行为识别

为了实现对人类行为的有效识别,提出了一种基于层次聚类多任务学习(HC-MTL)的人类行为识别方法。采用正则化最小二乘法制定目标函数,并对模型参数和分组信息这2个潜在的变量进行联合优化。使用聚类范数正则化方式进行多任务学习,并求解任务相关性,进而对人类行为进行有效识别。该方法打破了所有行为是独立的个人学习的假设,通过任务聚类的方式建立起多任务之间的关系,共享同类任务之间的相关信息,提高了人类行为识别的准确度。试验结果表明,与聚类多任务学习方法(CMTL)和鲁棒多任务学习方法(RMTL)相比,HC-MTL方法可以发现任务的潜在相关性,有助于诱导群体多任务学习。通过同一类任务之间的共享信息,减少了误差,并提高了行为识别的精确度。     

一种基于模糊规则融合的模糊建模方法及其应用

为了有效地利用经验知识,弥补训练数据覆盖范围不足的问题,提出一种将经验知识以TSK (Takagi-Sugeno-Kang)型模糊规则引入模糊模型的建模方法.在结构辨识中,提出了模糊规则融合方法,用以确定初始模糊规则.在参数辨识中,改进了原梯度下降方法中的目标函数,并引入了经验知识准确性评价参数,用以平衡样本数据和经验知识对模型的影响.数值仿真和工程实例应用结果表明,所提出的方法可以有效地利用经验知识和样本数据,使预报结果更可靠、更精确.     

显隐信息协同的多视角极限学习模糊系统

多视角数据正在越来越多地应用于各种建模任务,但当前的多视角模糊系统建模方法,主要集中于实现各个显性视角的合作,还未能充分探讨和利用各视角间共享的隐信息。针对此,对如何引入各个显性视角共享的隐空间信息来实现显隐视角协同的模糊系统建模进行了研究。具体地,基于岭回归极限学习模糊系统(ridge regression extreme learning fuzzy system,RR-EL-FS)模型,引入隐空间信息实现显隐视角协同学习来对RR-EL-FS进行学习,最终开发出具有显隐视角协同功能的岭回归极限学习模糊系统预测模型(ridgeregression extreme learning fuzzy system with cooperation between visible and hidden views,RR-EL-FS-CVH)。该方法较之以往相关的多视角建模方法能更好地利用隐空间的有效信息,从而能够进一步提高受训模型的泛化性能。大量的实验结果亦验证了所提方法的有效性。     

基于多任务学习的汉语基本篇章单元和主述位联合识别

基本篇章单元(elementary discourse units,EDU)识别是构建篇章结构的基础,对篇章分析意义重大。从篇章衔接性视角来看,篇章话题结构理论认为,每个EDU都由要表达信息的起始点(主位)和传达的新信息(述位)两部分构成。因此,EDU识别与主述位识别任务的关系密切。基于此,该文给出了一个基于多任务学习的汉语基本篇章单元和主述位联合识别方法。该方法利用双向长短时记忆网络和图卷积网络对基本单元进行序列化和结构化拓扑信息的表征,再利用多任务学习框架让两个任务共享参数,借助不同任务间的相关性来提升模型的性能。实验结果表明,基于多任务学习的EDU和主述位识别性能均优于单任务学习模型中各自的性能,其中基本篇章单元识别的F₁值达到91.90%,主述位识别的F₁值达到85.65%。     

基于TSK模糊系统的生化变量预估模型

提出了一种利用TSK(Takagi-Sugeno-Kang)模糊系统建立生化变量预估模型的方法,用于生化过程的工艺参数预测。利用TSK模糊系统的非线性逼近能力,以谷氨酸发酵过程为研究对象,建立了基于TSK模糊逻辑系统的生化变量预估器。以工厂现场采集的发酵过程参数大量数据为样本,训练TSK模糊系统生化变量预估器,并对模型的预估精度进行检验。仿真结果显示了预估模型的有效性,可以有效地预测谷氨酸发酵过程中生化变量的估计值。TSK模糊系统生化变量预估模型能预估生化过程中工业发酵罐的放罐时间,预估模型的状态预报对正常罐批具有足够高的预报精度,预报误差如果偏大亦可作为异常罐批的早期警示信息。有鉴于此,TSK模糊逻辑系统可望开辟生化过程参数预估的新途径。     

利用模糊系统的自适应模糊控制器

针对非线性系统控制,设计了利用TSK(Takagi-Sugeno-Kang)模糊系统的自适应模糊控制器。所设计的自适应控制方法是参考模型自适应控制方法,而且利用Lyapunov函数保证了闭环系统的稳定性,同时推导了最优的自适应控制规律。首先,根据控制对象的输入输出数据建立TSK模糊模型,然后,由TSK模糊模型设计初期的TSK模糊控制器,并根据自适应规律随时调整模糊控制器参数。倒立摆系统的仿真实验验证了所设计的自适应模糊控制器的有效性。     

贝叶斯L2型TSK模糊系统

针对传统Takagi-Sugeno-Kan(TSK)模糊系统处理大规模数据时间代价较高的问题,提出一种基于概率模型框架的L2型TSK模糊系统建模策略,建立具有处理大规模数据能力的贝叶斯L2型TSK模糊系统(B-TSK-FS).具体地,基于L2型TSK模糊系统的输出误差概率化表示,对系统前后件参数联合学习,提高系统的泛化能力.另外,引入狄利克雷先验分布函数,对模糊隶属度稀疏化表示,实现样本的压缩,降低运算时间.在模拟和真实数据集上的实验结果验证了所提出模糊系统的优势.     

Bayesian zero-order TSK fuzzy system modeling

Different from the existing TSK fuzzy system modeling methods, a novel zero-order TSK fuzzy modeling method called Bayesian zero-order TSK fuzzy system (B-ZTSK-FS) is proposed from the perspective of Bayesian inference in this paper. The proposed method B-ZTSK-FS constructs zero-order TSK fuzzy system by using the maximum a posteriori (MAP) framework to maximize the corresponding posteriori probability. First, a joint likelihood model about zero-order TSK fuzzy system is defined to derive a new objective function which can assure that both antecedents and consequents of fuzzy rules rather than only their antecedents of the most existing TSK fuzzy systems become interpretable. The defined likelihood model is composed of three aspects: clustering on the training set for antecedents of fuzzy rules, the least squares (LS) error for consequent parameters of fuzzy rules, and a Dirichlet prior distribution for fuzzy cluster memberships which is considered to not only automatically match the “sum-to-one” constraints on fuzzy cluster memberships, but also make the proposed method B-ZTSK-FS scalable for large-scale datasets by appropriately setting the Dirichlet index. This likelihood model indeed indicates that antecedent and consequent parameters of fuzzy rules can be linguistically interpreted and simultaneously optimized by the proposed method B-ZTSK-FS which is based on the MAP framework with the iterative sampling algorithm, which in fact implies that fuzziness and probability can co-jointly work for TSK fuzzy system modeling in a collaborative rather than repulsive way. Finally, experimental results on 28 synthetic and real-world datasets are reported to demonstrate the effectiveness of the proposed method B-ZTSK-FS in the sense of approximation accuracy, interpretability and scalability.     

GSETSK: a generic self-evolving TSK fuzzy neural network with a novel Hebbian-based rule reduction approach

Takagi–Sugeno–Kang (TSK) fuzzy systems have been widely applied for solving function approximation and regression-centric problems. Existing dynamic TSK models proposed in the literature can be broadly classified into two classes. Class I TSK models are essentially fuzzy systems that are limited to time-invariant environments. Class II TSK models are generally evolving systems that can learn in time-variant environments. This paper attempts to address the issues of achieving compact, up-to-date fuzzy rule bases and interpretable knowledge bases in TSK models. It proposes a novel rule pruning method which is simple, computationally efficient and biologically plausible. This rule pruning algorithm applies a gradual forgetting approach and adopts the Hebbian learning mechanism behind the long-term potentiation phenomenon in the brain. It also proposes a merging approach which is used to improve the interpretability of the knowledge bases. This approach can prevent derived fuzzy sets from expanding too many times to protect their semantic meanings. These two approaches are incorporated into a generic self-evolving Takagi–Sugeno–Kang fuzzy framework (GSETSK) which adopts an online data-driven incremental-learning-based approach.Extensive experiments were conducted to evaluate the performance of the proposed GSETSK against other established evolving TSK systems. GSETSK has also been tested on real world dataset using the high-way traffic flow density and Dow Jones index time series. The results are encouraging. GSETSK demonstrates its fast learning ability in time-variant environments. In addition, GSETSK derives an up-to-date and better interpretable fuzzy rule base while maintaining a high level of modeling accuracy at the same time.     

Online identification of evolving Takagi–Sugeno–Kang fuzzy models for crane systems

This paper suggests new evolving Takagi–Sugeno–Kang (TSK) fuzzy models dedicated to crane systems. A set of evolving TSK fuzzy models with different numbers of inputs are derived by the novel relatively simple and transparent implementation of an online identification algorithm. An input selection algorithm to guide modeling is proposed on the basis of ranking the inputs according to their important factors after the first step of the online identification algorithm. The online identification algorithm offers rule bases and parameters which continuously evolve by adding new rules with more summarization power and by modifying existing rules and parameters. The potentials of new data points are used with this regard. The algorithm is applied in the framework of the pendulum–crane system laboratory equipment. The evolving TSK fuzzy models are tested against the experimental data and a comparison with other TSK fuzzy models and modeling approaches is carried out. The comparison points out that the proposed evolving TSK fuzzy models are simple and consistent with both training data and testing data and that these models outperform other TSK fuzzy models.     

Incremental modeling with rough and fine tuning method

In this paper, we propose a new learning approach for designing an incremental model that has a cascade learning structure combined with a rough and fine tuning method for the learning scheme. Recently, various fuzzy logic-based modeling methods, with fuzzy if-then type rules, have been proposed in an attempt to obtain good approximations and generalization performances. In contrast to these various modeling methods, the new proposed incremental modeling scheme presented here is combined with a rough and fine tuning scheme, to learn and construct the best architecture for the model. A compensation idea is introduced in the fine tuning stage to solve the over-fitting problem caused from testing data. For this purpose, a construct of an extreme learning machine (ELM) is used as a global model, and this is compensated through a conditional fuzzy C-means (CFCM)-based fuzzy inference system (FIS) with a Takagi–Sugeno–Kang (TSK)-type method, which captures the remaining localized nonlinearities of the model. The experimental results, obtained by the proposed model have proved to show better performances in comparison with previous works.     

Chaotic time series prediction via artificial neural square fuzzy inference system

The present article investigates the application of second order TSK (Takagi Sugeno Kang) fuzzy systems in predicting chaotic time series. A method has been introduced for training second order TSK fuzzy systems using ANFIS (Artificial Neural Fuzzy Inference System) training method. In a second order TSK system existence of nonlinear terms in the rules’ consequence prohibits use of current available ANFIS codes as is but the proposed method makes it possible to use ANFIS for a class of simplified second order TSK systems. The main impact of this method on the expert and intelligent systems is to provide a new way for modeling and predicting the future situation of more complex phenomena with a smaller decision rule base. The most significance of the proposed method is the simplicity and available code reuse property. As a case study the proposed method is used for the prediction of chaotic time series. Error comparison shows that the proposed method trains the second order TSK system more effectively.     

Nature-inspired optimal tuning of input membership functions of Takagi-Sugeno-Kang fuzzy models for Anti-lock Braking Systems

This paper suggests a synergy of fuzzy logic and nature-inspired optimization in terms of the nature-inspired optimal tuning of the input membership functions of a class of Takagi-Sugeno-Kang (TSK) fuzzy models dedicated to Anti-lock Braking Systems (ABSs). A set of TSK fuzzy models is proposed by a novel fuzzy modeling approach for ABSs. The fuzzy modeling approach starts with the derivation of a set of local state-space models of the nonlinear ABS process by the linearization of the first-principle process model at ten operating points. The TSK fuzzy model structure and the initial TSK fuzzy models are obtained by the modal equivalence principle in terms of placing the local state-space models in the rule consequents of the TSK fuzzy models. An operating point selection algorithm to guide modeling is proposed, formulated on the basis of ranking the operating points according to their importance factors, and inserted in the third step of the fuzzy modeling approach. The optimization problems are defined such that to minimize the objective functions expressed as the average of squared modeling errors over the time horizon, and the variables of these functions are a part of the parameters of the input membership functions. Two representative nature-inspired algorithms, namely a Simulated Annealing (SA) algorithm and a Particle Swarm Optimization (PSO) algorithm, are implemented to solve the optimization problems and to obtain optimal TSK fuzzy models. The validation and the comparison of SA and PSO and of the new TSK fuzzy models are carried out for an ABS laboratory equipment. The real-time experimental results highlight that the optimized TSK fuzzy models are simple and consistent with both training data and validation data and that these models outperform the initial TSK fuzzy models.     

多层递阶融合模糊特征映射的模糊C均值聚类算法

针对复杂非线性数据的无监督学习问题,提出一种新型的映射方式来有效提高算法对复杂非线性数据的学习能力。以TSK模糊系统的规则前件学习为基础,提出一种新型的模糊特征映射新方法。接着,针对映射之后的数据维度过大问题,引入多层递阶融合的概念,进一步提出基于多层递阶融合的模糊特征映射新方法,从而有效避免了因单层模糊特征映射之后特征维数过高而导致的数据混乱和冗余的问题。最后与模糊C均值算法相结合,提出基于多层递阶融合模糊特征映射的模糊C均值聚类算法。实验研究表明,文中算法相比于经典模糊聚类方法,有着更加优越、稳定的性能。     

Robust TSK fuzzy modeling for function approximation with outliers

The Takagi-Sugeno-Kang (TSK) type of fuzzy models has attracted a great attention of the fuzzy modeling community due to their good performance in various applications. Most approaches for modeling TSK fuzzy rules define their fuzzy subspaces based on the idea of training data being close enough instead of having similar functions. Besides, training data sets algorithms often contain outliers, which seriously affect least-square error minimization clustering and learning algorithms. A robust TSK fuzzy modeling approach is presented. In the approach, a clustering algorithm termed as robust fuzzy regression agglomeration (RFRA) is proposed to define fuzzy subspaces in a fuzzy regression manner with robust capability against outliers. To obtain a more precision model, a robust fine-tuning algorithm is then employed. Various examples are used to verify the effectiveness of the proposed approach. From the simulation results, the proposed robust TSK fuzzy modeling indeed showed superior performance over other approaches     

基于模糊子空间聚类的0 阶岭回归TSK 模糊系统

经典数据驱动型TSK模糊系统在利用高维数据训练模型时,由于规则前件采用的特征过多,导致规则的解释性和简洁性下降.对此,根据模糊子空间聚类算法的子空间特性,为TSK模型添加特征抽取机制,并进一步利用岭回归实现后件的学习,提出一种基于模糊子空间聚类的0阶岭回归TSK模型构建方法.该方法不仅能为规则抽取出重要子空间特征,而且可为不同规则抽取不同的特征.在模拟和真实数据集上的实验结果验证了所提出方法的优势.