Interval type-2 neuro-fuzzy system with implication-based inference mechanism

Neuro-fuzzy systems have been proved to be an efficient tool for modelling real life systems. They are precise and have ability to generalise knowledge from presented data. Neuro-fuzzy systems use fuzzy sets – most commonly type-1 fuzzy sets. Type-2 fuzzy sets model uncertainties better than type-1 fuzzy sets because of their fuzzy membership function. Unfortunately computational complexity of type reduction in general type-2 systems is high enough to hinder their practical application. This burden can be alleviated by application of interval type-2 fuzzy sets. The paper presents an interval type-2 neuro-fuzzy system with interval type-2 fuzzy sets both in premises (Gaussian interval type-2 fuzzy sets with uncertain fuzziness) and consequences (trapezoid interval type-2 fuzzy set). The inference mechanism is based on the interval type-2 fuzzy Łukasiewicz, Reichenbach, Kleene-Dienes, or Brouwer–Gödel implications. The paper is accompanied by numerical examples. The system can elaborate models with lower error rate than type-1 neuro-fuzzy system with implication-based inference mechanism. The system outperforms some known type-2 neuro-fuzzy systems.     

Application of type-2 neuro-fuzzy modeling in stock price prediction

We present an application of type-2 neuro-fuzzy modeling to stock price prediction based on a given set of training data. Type-2 fuzzy rules can be generated automatically by a self-constructing clustering method and the obtained type-2 fuzzy rules cab be refined by a hybrid learning algorithm. The given training data set is partitioned into clusters through input-similarity and output-similarity tests, and a type-2 TSK rule is derived from each cluster to form a fuzzy rule base. Then the antecedent and consequent parameters associated with the rules are refined by particle swarm optimization and least squares estimation. Experimental results, obtained by running on several datasets taken from TAIEX and NASDAQ, demonstrate the effectiveness of the type-2 neuro-fuzzy modeling approach in stock price prediction.     

Optimal design of adaptive type-2 neuro-fuzzy systems: A review

Type-2 fuzzy logic systems have extensively been applied to various engineering problems, e.g. identification, prediction, control, pattern recognition, etc. in the past two decades, and the results were promising especially in the presence of significant uncertainties in the system. In the design of type-2 fuzzy logic systems, the early applications were realized in a way that both the antecedent and consequent parameters were chosen by the designer with perhaps some inputs from some experts. Since 2000s, a huge number of papers have been published which are based on the adaptation of the parameters of type-2 fuzzy logic systems using the training data either online or offline. Consequently, the major challenge was to design these systems in an optimal way in terms of their optimal structure and their corresponding optimal parameter update rules. In this review, the state of the art of the three major classes of optimization methods are investigated: derivative-based (computational approaches), derivative-free (heuristic methods) and hybrid methods which are the fusion of both the derivative-free and derivative-based methods.     

H-BSP: A Hierarchical BSP Computation Model

This paper presents a new parallel computing model, called H-BSP, which adds a hierarchical concept to the BSP(Bulk Synchronous Parallel) computing model. An H-BSP program consists of a number of BSP groups which are dynamically created at run time and executed in a hierarchical fashion. H-BSP allows algorithm designers to develop more efficient algorithms by utilizing processor locality in the program. Based on the distributed memory model, H-BSP provides a group-based programming paradigm and supports Divide & Conquer algorithms efficiently. This paper describes the structure of the H-BSP model, complexity analysis and some examples of H-BSP algorithm. Also presented is the performance characteristics of H-BSP algorithms based on the simulation analysis. Simulation results show that H-BSP takes advantages of processor locality and performs well in low bandwidth networks or in a constant-valence architecture such as 2-dimensional mesh. It is also proved that H-BSP can predict algorithm performance better than BSP, due to its locality-preserving nature.     

Simplifying a neuro-fuzzy model

Neuro-fuzzy modeling allows a fuzzy system to be refined by neural training, thus avoiding lenghty trial-and-error phases in defining both membership functions and inference rules. An approach to obtain simple neuro-fuzzy models is proposed, which reduces the number of rules by means of a systematic procedure that consists in successively removing a rule and updating the remaining rules in such a way that the overall input-output behavior is kept approximately unchanged over the entire training set. A formulation of the proper update is described and a criterion for choosing the rules to be removed is also provided. Initial experimental results show the effectiveness of the proposed method in reducing the complexity of a neuro-fuzzy system by using its input-output data.     

Hierarchical neuro-fuzzy current control for a shunt active power filter

This paper presents the design of a hierarchical neuro-fuzzy current control scheme for a shunt active power filter compared with a single fuzzy controller scheme. A single fuzzy controller scheme is presented first and an ANFIS based neuro-fuzzy controller is connected hierarchically to the first one to improve the performance. Simulation results show that harmonic compensation is achieved with both schemes, however switching performance is superior for hierarchical scheme. The method of switching controller development in this paper is new and can be applied to other power electronic converter applications for improved performance.     

Relation-based computations in a monadic BSP model

We propose a Haskell monadic model of bulk synchronous parallel programs and apply it to the analysis of relation-based computations.

Relation-based computations are simple but general patterns found in scientific computing applications. They are easy to implement sequentially, but difficult to parallelize.

We use the model to give high-level specifications of distributed relation-based algorithms and outline how to obtain testable parallel implementations from these specifications via equational reasoning.

We sketch the architecture of a C++ library of components for distributed relation-based computations. We argue that the model can be used to provide a concise and consistent library documentation.     

二型Takagi-Sugeno-Kang模糊模型和不确定高斯混合模型的等价性

不确定的高斯混合模型和二型Takagi-Sugeno-Kang(TSK)模糊模型之间的对应关系被建立: 任何一个不确定的高斯混合模型都唯一对应着一个二型TSK模糊系统, 不确定的高斯混合模型的条件均值和二型TSK模糊 系统的输出是等价的. 基于此, 一种设计二型模糊系统的新方法被提出: 通过建立不确定的高斯混合模型确定二型TSK模糊系统, 即用概率统计的方法设计二型模糊系统. 仿真实验结果表明利用不确定高斯混合模型设计的二型模糊系统比其它模型具有更强的抗噪性和更快的速度.     

A programming model for BSP with partitioned synchronisation

A BSP superstep is a distributed computation comprising a number of simultaneously executing processes which may generate asynchronous messages. A superstep terminates with a barrier which enforces a global synchronisation and delivers all ongoing communications. Multilevel supersteps can utilise barriers in which subsets of processes, interacting through shared memories, are locally synchronised (partitioned synchronisation). In this paper a state-based semantics, closely related to the classical sequential programming model, is derived for distributed BSP with partitioned synchronisation.     

Applying neuro-fuzzy model dFasArt in control systems

This paper presents the neuro-fuzzy dFasArt (dynamic FasArt) architecture as an extension of the FasArt model including a dynamic algorithm formulation. This allows dFasArt to deal with identification and clustering problems using the temporal information of the signals. The focus is placed on the application of dFasArt to the control systems field for monitoring the controller performance. It is presented through two selected experiments covering some interesting control issues. The first one shows the use of dFasArt to decide when the parameters adaption is needed in a classic adaptive control scheme. The second one analyzes the behaviour of closed-loop controlled systems to establish a classification of the system operational states, starting from the measured data. Digital signal processing is used to represent the temporal signals with spatial patterns and dFasArt is proposed to classify these patterns on-line. Real scale plants have been used to carry out several experiments with good results. This shows dFasArt as a feasible tool to deal with control loop performance monitoring and controller performance assessment in industrial processes.     

Improving the COCOMO model using a neuro-fuzzy approach

Accurate software development cost estimation is important for effective project management such as budgeting, project planning and control. So far, no model has proved to be successful at effectively and consistently predicting software development cost. A novel neuro-fuzzy Constructive Cost Model (COCOMO) is proposed for software cost estimation. This model carries some of the desirable features of a neuro-fuzzy approach, such as learning ability and good interpretability, while maintaining the merits of the COCOMO model. Unlike the standard neural network approach, the proposed model can be interpreted and validated by experts, and has good generalization capability. The model deals effectively with imprecise and uncertain input and enhances the reliability of software cost estimates. In addition, it allows input to have continuous rating values and linguistic values, thus avoiding the problem of similar projects having large different estimated costs. A detailed learning algorithm is also presented in this work. The validation using industry project data shows that the model greatly improves estimation accuracy in comparison with the well-known COCOMO model.     

基于SVD 方法的简约型区间二型模糊模型构建

针对奇异值-QR分解方法存在有效奇异值难以确定的问题,采用奇异值分解方法分析从区间二型模糊模型抽取的两个激活强度矩阵,提出了奇异值归一化差值的概念以描述相邻奇异值的变化情况,从而反映了重要规则和冗余规则在奇异值变化上的本质差异;进而根据其临界点确定有效奇异值个数,并利用QR分解得到有效奇异值所对应的重要规则构建简约型区间二型模糊结构。仿真实例验证了所提出方法的有效性和可行性。     

Correlation analysis method based SISO neuro-fuzzy Wiener model

A novel identification algorithm for neuro-fuzzy based single-input-single-output (SISO) Wiener model with colored noises is presented in this paper. The separable signal is adopted to identify the Wiener model, leading to the identification problem of the linear part separated from nonlinear counterpart. Then, the correlation analysis method can be employed for identification of linear part. Moreover, in the presence of random signal, the least square method based parameters estimation algorithm of static nonlinear part are proposed to avoid the impact of colored noise. As a result, proposed method can circumvent the problem of initialization and convergence of the model parameters encountered by the existing iterative algorithms used for identification of Wiener model. Examples are used to verify the effectiveness of the proposed method.     

基于数据的间歇过程时变神经模糊模型研究

间歇过程的优化控制往往依赖于过程精确的数学模型,快速反应的市场要求使得数据驱动的建模方法被应用到了间歇过程的建模中.但常规的数据驱动建模方法在模型结构中没有考虑间歇过程具有重复性的特性,只是简单地将间歇过程作为一般的非线性结构进行处理.针对该问题,本文提出一种新颖的间歇过程时变神经模糊模型,将时间轴和批次轴的信息统一在...     

含有色噪声的神经模糊Hammerstein模型分离辨识

针对实际工业过程中普遍存在的有色噪声,本文提出一种基于递推增广最小二乘算法的神经模糊Hammerstein模型辨识方法,突破了传统的Hammerstein模型迭代分离算法.首先,利用多信号源实现Hammerstein模型中静态非线性环节和动态线性环节的分离,大大简化了辨识过程,提高了串联环节参数的分离精度.其次,利用长除法将噪声模型用有限脉冲响应模型逼近,采用增广递推最小二乘法进行线性环节的参数估计.最后,采用神经模糊模型拟合静态非线性环节,同时设计了神经模糊模型参数的非迭代优化算法,改善了模型的使用范围.该方法保证了模型的预测精度,对含有色噪声的非线性系统具有较好的拟合效果.仿真结果验证了上述方法的有效性.     

A type-2 fuzzy rule-based expert system model for stock price analysis

In this paper, a type-2 fuzzy rule based expert system is developed for stock price analysis. Interval type-2 fuzzy logic system permits us to model rule uncertainties and every membership value of an element is interval itself. The proposed type-2 fuzzy model applies the technical and fundamental indexes as the input variables. This model is tested on stock price prediction of an automotive manufactory in Asia. Through the intensive experimental tests, the model has successfully forecasted the price variation for stocks from different sectors. The results are very encouraging and can be implemented in a real-time trading system for stock price prediction during the trading period.     

Correlation analysis based MIMO neuro-fuzzy Hammerstein model with noises

A novel identification algorithm for neuro-fuzzy based MIMO Hammerstein system with noises by using the correlation analysis method is presented in this paper. A special test signal that contains independent separable signals and uniformly random multi-step signal is adopted to identify the MIMO Hammerstein system, resulting in the identification problem of the linear model separated from that of nonlinear part. As a result, it can circumvent the problem of initialization and convergence of the model parameters encountered by the existing iterative algorithms used for identification of MIMO Hammerstein model. Moreover, least square method based parameter identification algorithms of dynamic linear part and static nonlinear part are proposed to avoid the influence of noise. Examples are used to illustrate the effectiveness of the proposed method.     

Using the BSP cost model to optimise parallel neural network training

We derive cost formulae for three different parallelisation techniques for training both supervised and unsupervised networks. These formulae are parameterised by properties of the target computer architecture. It is therefore possible to decide both which technique is best for a given parallel computer, and which parallel computer best suits a given technique. One technique, exemplar parallelism, is far superior to almost all parallel computer architectures. Formulae also take into account optimal batch learning as the overall training approach. Cost predictions are made for several of today's popular parallel computers.     

Extending the BSP model for multi-core and out-of-core computing: MBSP

We present an extension of the bulk-synchronous parallel (BSP) model to abstract and model parallelism in the presence of multiple memory hierarchies and multiple cores. We call the new model MBSP for multi-memory BSP. The BSP model has been used to model internal memory parallel computers; MBSP retains the properties of BSP and in addition can abstract not only traditional external memory-supported parallelism (e.g. that uses another level of slower memory) but also multi-level cache-based memory hierarchies such as those present in multi-core systems. Present day multi-core systems are limited parallelism architectures with fast inter-core communication but limited fast memory availability. Abstracting the programming requirements of such architectures in a useful and usable manner is the objective of introducing MBSP. We propose multi-core program and algorithm design that measures resource utilization through a septuplet $(p\text{,}l\text{,}g\text{,}m\text{,}L\text{,}G\text{,}M)$ in which $(p\text{,}l\text{,}g)$ are the BSP parameters for modeling processor component size and interprocessor communication through latency-based and throughput-based cost mechanisms, and $(m\text{,}L\text{,}G\text{,}M)$ are the new parameters that abstract additional memory hierarchies. Each processor component is attached to a memory of size M, and there are also m memory-units accessing a slower memory of unlimited size of latency-based and throughput-based cost $(L\text{,}G)$. A deterministic sorting algorithm is described on this model that is potentially both usable and useful.     

A simple and efficient parallel FFT algorithm using the BSP model

We present a new parallel radix-4 FFT algorithm based on the BSP model. Our parallel algorithm uses the group-cyclic distribution family, which makes it simple to understand and easy to implement. We show how to reduce the communication cost of the algorithm by a factor of 3, in the case that the input/output vector is in the cyclic distribution. We also show how to reduce computation time on computers with a cache-based architecture. We present performance results on a Cray T3E with up to 64 processors, obtaining reasonable efficiency levels for local problem sizes as small as 256 and very good efficiency levels for local sizes larger than 2048.