Adaptive fuzzy neuro-observer applied to low cost INS/GPS

A combined MEMS Inertial Navigation System (INS) with GPS is used to provide position and velocity data of land vehicles. Data fusion of INS and GPS measurements are commonly achieved through a conventional Extended Kalman filter (EKF). Considering the required accurate model of system together with perfect knowledge of predefined error models, the performance of the EKF is decreased due to unmodeled nonlinearities and unknown bias uncertainties of MEMS inertial sensors. Universal knowledge based approximators comprising of neural networks and fuzzy logic methods are capable of approximating the nonlinearities and the uncertainties of practical systems. First, in this paper, a new fuzzy neural network (FNN) function approximator is used to model unknown nonlinear systems. Second, the process of design and real-time implementation of an adaptive fuzzy neuro-observer (AFNO) in integrated low-cost INS/GPS positioning systems is proposed. To assess the long time performance of the proposed AFNO method, wide range tests of a real INS/GPS with a car vehicle have been performed. The unbiased estimation results of the AFNO show the superiority of the proposed method compared with the classic EKF and the adaptive neuro-observer (ANO) including a pure artificial neural network (ANN) function approximator.     

Modelling,control, and stability analysis of non-linear systems using generalized fuzzy neural networks

This paper presents an adaptive fuzzy neural controller (AFNC) suitable for modelling and control of MIMO non-linear dynamic systems. The proposed AFNC has the following salient features: (1) fuzzy neural control rules can be generated or deleted dynamically and automatically; (2) uncertain MIMO non-linear systems can be adaptively modelled on line; (3) adaptation and learning speed is fast; (4) expert knowledge can be easily incorporated into the system; (5) the structure and parameters of the AFNC can be self-adaptive in the presence of uncertainties to maintain a high control performance; and (6) the asymptotical stability of the system is established using the Lyapunov approach. Simulation studies on a two-link robot manipulator show that the performance of the proposed controller is better than that of some existing fuzzy/neural methods.     

Handwritten Chinese Character Recognition Based on Primitive and Fuzzy Features via the SEART Neural Net Model

A handwritten Chinese character recognition method based on primitive and compound fuzzy features using the SEART neural network model is proposed. The primitive features are extracted in local and global view. Since handwritten Chinese characters vary a great deal, the fuzzy concept is used to extract the compound features in structural view. We combine the two categories of features and use a fast classifier, called the Supervised Extended ART (SEART) neural network model, to recognize handwritten Chinese characters. The SEART classifier has excellent performance, is fast, and has good generalization and exception handling abilities in complex problems. Using the fuzzy set theory in feature extraction and the neural network model as a classifier is helpful for reducing distortions, noise and variations. In spite of the poor thinning, a 90.24% recognition rate on average for the 605 test character categories was obtained. The database used is CCL/HCCR3 (provided by CCL, ITRI, Taiwan). The experiment not only confirms the feasibility of the proposed system, but also suggests that applying the fuzzy set theory and neural networks to recognition of handwritten Chinese characters is an efficient and promising approach.     

Neurofuzzy identification of an autonomous underwater vehicle

Neurofuzzy modelling is ideally suited to many nonlinear system identification and data modelling applications. By combining the attractive attributes of fuzzy systems and neural networks transparent models of ill-defined systems can be identified. Available expert a priori knowledge is used to construct an initial model. Data modelling techniques from the neural network, statistical and conventional system identification communities are then used to adapt these models. As a result accurate parsimonious models which are transparent and easy to validate are identified. Recent advances in the datadriven identification algorithms have now made neurofuzzy modelling appropriate for high-dimensional problems for which the expert knowledge and data may be of a poor quality. In this paper neurofuzzy modelling techniques are presented. This powerful approach to system identification is demonstrated by its application to the identification of an Autonomous Underwater Vehicle (AUV).     

Recursive Bayesian Recurrent Neural Networks for Time-Series Modeling

This paper develops a probabilistic approach to recursive second-order training of recurrent neural networks (RNNs) for improved time-series modeling. A general recursive Bayesian Levenberg–Marquardt algorithm is derived to sequentially update the weights and the covariance (Hessian) matrix. The main strengths of the approach are a principled handling of the regularization hyperparameters that leads to better generalization, and stable numerical performance. The framework involves the adaptation of a noise hyperparameter and local weight prior hyperparameters, which represent the noise in the data and the uncertainties in the model parameters. Experimental investigations using artificial and real-world data sets show that RNNs equipped with the proposed approach outperform standard real-time recurrent learning and extended Kalman training algorithms for recurrent networks, as well as other contemporary nonlinear neural models, on time-series modeling.      

Parametric design with neural network relationships and fuzzy relationships considering uncertainties

This research introduces a new parametric design approach with neural network relationships and fuzzy relationships considering uncertainties. In this work, parameters are associated by a hybrid parameter relationship network. In addition to deterministic parameters and relationships, non-deterministic parameters (e.g., random parameters and fuzzy parameters) and non-deterministic relationships (e.g., neural network relationships and fuzzy relationships) can also be modeled in this network. Changes of parameter values and their uncertainties are propagated through this network. Two types of optimization methods, reliability based design optimization and possibility based design optimization, are employed to identify the optimal design considering objective random uncertainties and subjective fuzzy uncertainties. A computer system has been implemented and used for the optimal design of a solid oxide fuel cell (SOFC) system considering uncertainties.     

Fuzzy Techniques for Subjective Workload-Score Modeling Under Uncertainties

This paper deals with the development of a computer model to estimate the subjective workload score of individuals by evaluating their heart-rate (HR) signals. The identification of a model to estimate the subjective workload score of individuals under different workload situations is too ambitious a task because different individuals (due to different body conditions, emotional states, age, gender, etc.) show different physiological responses (assessed by evaluating the HR signal) under different workload situations. This is equivalent to saying that the mathematical mappings between physiological parameters and the workload score are uncertain. Our approach to deal with the uncertainties in a workload-modeling problem consists of the following steps: 1) The uncertainties arising due the individual variations in identifying a common model valid for all the individuals are filtered out using a fuzzy filter; 2) stochastic modeling of the uncertainties (provided by the fuzzy filter) use finite-mixture models and utilize this information regarding uncertainties for identifying the structure and initial parameters of a workload model; and 3) finally, the workload model parameters for an individual are identified in an online scenario using machine learning algorithms. The contribution of this paper is to propose, with a mathematical analysis, a fuzzy-based modeling technique that first filters out the uncertainties from the modeling problem, analyzes the uncertainties statistically using finite-mixture modeling, and, finally, utilizes the information about uncertainties for adapting the workload model to an individual's physiological conditions. The approach of this paper, demonstrated with the real-world medical data of 11 subjects, provides a fuzzy-based tool useful for modeling in the presence of uncertainties.      

Control-affine fuzzy neural network approach for nonlinear process control

An internal model control strategy employing a fuzzy neural network is proposed for SISO nonlinear process. The control-affine model is identified from both steady state and transient data using back-propagation. The inverse of the process is obtained through algebraic inversion of the process model. The resulting model is easier to interpret than models obtained from the standard neural network approaches. The proposed approach is applied to the tasks of modelling and control of a continuous stirred tank reactor and a pH neutralization process which are not inherently control-affine. The results show a significant performance improvement over a conventional PID controller. In addition, an additional neural network which models the discrepancy between a control-affine model and real process dynamics is added, and is shown to lead to further improvement in the closed-loop performance.     

Design of stochastic discrete time linear optimal regulators Part I. Relationship between control laws based on a time series approach and a state space approach

Distinct approaches to sampled data control system design use either a state space model or a ‘ controlled autoregressive moving average ’ (CARMA) model, sometimes known as Åström's representation, One reason for the current interest in the CARMA model is that it is a useful basis for self-tuning controllers as its parameters can be readily estimated on-line. Moreover, simple transfer function controllers can be derived using κ-step-ahead prediction theory. On the other hand, these controllers can be interpreted as minimizing a single stage cost function in state space terms, and the corresponding performance can sometimes be poor. This paper explores the relationship between the κ-stop-ahead prediction approach and the state space approach, and is a generalization of the earlier work of Caines to include control weighting and time delay on the control. Two forms of state space model are used (‘ explicit ’ and ‘ implicit’ time delay models) and a new representation of the steady state Kalman filter is shown to be required in the generalization ; this filter is put into a computationally convenient form. The technical machinery created in this paper allows for a straightforward generalization to the control of systems described by CARMA models by minimizing an N-stage cost function ; this generalization will be discussed in paper II     

Gaussian process internal model control

Nonlinear modelling approaches such as neural networks, fuzzy models and multiple model networks have been proposed for model based control, to improve the poor transient response of adaptive control techniques. The quality of control is known to be strongly related to the accuracy of the model which represents the process. A Bayesian Gaussian process (GP) approach provides an analytic prediction of the model uncertainty, which makes the GP model an ideal candidate for model based control strategies. This article extends the use of the GP model for nonlinear internal model control. The invertibility of the GP model is discussed and the use of predicted variance is illustrated on a simulated example.     

A Fuzzy Modelling Approach Using Hierarchical Neural Networks

A simple and effective fuzzy modelling approach is presented in this paper. A three-layer hierarchical clustering neural network is developed to build fuzzy rule-based models from numerical data. Differing from existing clustering-based methods, in this approach the structure identification of the fuzzy model is implemented on the basis of a class of sub-clusters created by a self-organising network instead of on raw data. By combined use of unsupervised and supervised learning, both structure identification and parameter optimisation of the fuzzy model can be carried out automatically. The simulation results show that the proposed method can provide good model structure for fuzzy modelling and has high computing efficiency.     

Fault Detection for Fuzzy Systems With Intermittent Measurements

This paper investigates the problem of fault detection for Takagi-Sugeno (T-S) fuzzy systems with intermittent measurements. The communication links between the plant and the fault detection filter are assumed to be imperfect (i.e., data packet dropouts occur intermittently, which appear typically in a network environment), and a stochastic variable satisfying the Bernoulli random binary distribution is utilized to model the unreliable communication links. The aim is to design a fuzzy fault detection filter such that, for all data missing conditions, the residual system is stochastically stable and preserves a guaranteed performance. The problem is solved through a basis-dependent Lyapunov function method, which is less conservative than the quadratic approach. The results are also extended to T--S fuzzy systems with time-varying parameter uncertainties. All the results are formulated in the form of linear matrix inequalities, which can be readily solved via standard numerical software. Two examples are provided to illustrate the usefulness and applicability of the developed theoretical results.     

Production modelling for holistic production control

Holistic production control is a concept that introduces production optimisation by employing model-based, closed-loop control of the principal production Performance Indicators (pPIs). The concept relies on the development of a simple black-box model that describes the relation between the main pPIs and the most influential input (manipulative) variables. In this article the modelling aspects of the holistic production control implementation are presented. The main steps of the production modelling procedure are described, such as data preprocessing, the definition of pPIs, the selection of input variables and the derivation of black-box models. Particular emphasis is given to a modelling approach based on neural networks and a corresponding modelling assistant tool, which has been developed to support the modelling procedure. The approach is illustrated on the Tennessee Eastman benchmark process, where neural network models for three main production performance indicators, i.e., costs, quality and production rate, are derived.     

基于模糊RBF神经网络的非线性滤波

该文从基本的智能技术——神经网络(NN)和模糊系统(FS)技术出发,探讨了神经网络与模糊系统相结合的基本理论,提出了一种基于模糊RBF神经网络的非线性滤波的方法。该方法将模糊逻辑的知识表达以及推理能力和RBF网络的快速学习和泛化能力结合起来,网络结构参数可按实际问题调整,对信号中有色噪声进行较高精度的逼近,来达到非线性滤波的目的。该滤波方法显示出很强的处理问题的能力,学习速度快,仿真结果表明了这种方法的有效性和可性行。     

Fuzzy network model-based fuzzy state controller design

The performance of model-based controller design relies heavily on the quality and suitability of the utilized process model. This contribution proposes a fuzzy network based nonlinear controller design methodology. Fuzzy networks are a model approach combining high approximation quality with high interpretability. The input/output (I/O) models commonly used for identification are transformed to fuzzy state-space models. Transferring and adjusting methods from linear state-space theory a control concept consisting of a fuzzy state controller and an adaptive set-point filter for nonlinear dynamic processes is deduced. The capability of the method is demonstrated for a hydraulic drive     

A hybrid \text{ M}5^\prime -genetic programming approach for ensuring greater trustworthiness of prediction ability in modelling of FDM process

Recent years have seen various rapid prototyping (RP) processes such as fused deposition modelling (FDM) and three-dimensional printing being used for fabricating prototypes, leading to shorter product development times and less human intervention. The literature reveals that the properties of RP built parts such as surface roughness, strength, dimensional accuracy, build cost, etc are related to and can be improved by the appropriate settings of the input process parameters. Researchers have formulated physics-based models and applied empirical modelling techniques such as regression analysis and artificial neural network for the modelling of RP processes. Physics-based models require in-depth understanding of the processes which is a formidable task due to their complexity. The issue of improving trustworthiness of the prediction ability of empirical models on test (unseen) samples is paid little attention. In the present work, a hybrid M5 \(^{\prime }\) -genetic programming (M5 \(^{\prime }\) -GP) approach is proposed for empirical modelling of the FDM process with an attempt to resolve this issue of ensuring trustworthiness. This methodology is based on the error compensation achieved using a GP model in parallel with a M5 \(^{\prime }\) model. The performance of the proposed hybrid model is compared to those of support vector regression (SVR) and adaptive neuro fuzzy inference system (ANFIS) model and it is found that the M5 \(^{\prime }\) -GP model has the goodness of fit better than those of the SVR and ANFIS models.     

A generalized Takagi–Sugeno–Kang recurrent fuzzy-neural filter for adaptive noise cancelation

This paper presents a recurrent fuzzy-neural filter for adaptive noise cancelation. The cancelation task is transformed to a system-identification problem, which is tackled by use of the dynamic neuron-based fuzzy neural network (DN-FNN). The fuzzy model is based on Takagi–Sugeno–Kang fuzzy rules, whose consequent parts consist of linear combinations of dynamic neurons. The orthogonal least squares method is employed to select the number of rules, along with the number and kind of dynamic neurons that participate in each rule. Extensive simulation results are given and performance comparison with a series of other dynamic fuzzy and neural models is conducted, underlining the effectiveness of the proposed filter and its superior performance over its competing rivals.     

Analogy making in legal reasoning with neural networks and fuzzy logic

Analogy making from examples is a central task in intelligent system behavior. A lot of real world problems involve analogy making and generalization. Research investigates these questions by building computer models of human thinking concepts. These concepts can be divided into high level approaches as used in cognitive science and low level models as used in neural networks. Applications range over the spectrum of recognition, categorization and analogy reasoning. A major part of legal reasoning could be formally interpreted as an analogy making process. Because it is not the same as reasoning in mathematics or the physical sciences, it is necessary to use a method, which incorporates first the ability to specify likelihood and second the opportunity of including known court decisions. We use for modelling the analogy making process in legal reasoning neural networks and fuzzy systems. In the first part of the paper a neural network is described to identify precedents of immaterial damages. The second application presents a fuzzy system for determining the required waiting period after traffic accidents. Both examples demonstrate how to model reasoning in legal applications analogous to recent decisions: first, by learning a system with court decisions, and second, by analyzing, modelling and testing the decision making with a fuzzy system.     

Adaptive fuzzy neural network finite-time command filtered control of n-link robotic systems with actuator saturation

The finite time tracking control of n-link robotic system is studied for model uncertainties and actuator saturation. Firstly, a smooth function and adaptive fuzzy neural network online learning algorithm are designed to address the actuator saturation and dynamic model uncertainties. Secondly, a new finite-time command filtered technique is proposed to filter the virtual control signal. The improved error compensation signal can reduce the impact of filtering errors, and the tracking errors of system quickly converge to a smaller compact set within finite time. Finally, adaptive fuzzy neural network finite-time command filtered control achieves finite-time stability through Lyapunov stability criterion. Simulation results verify the effectiveness of the proposed control.     

Artificial neural network and wavelet neural network approaches for modelling of a solar air heater

This paper reports on a modelling study of new solar air heater (SAH) system by using artificial neural network (ANN) and wavelet neural network (WNN) models. In this study, a device for inserting an absorbing plate made of aluminium cans into the double-pass channel in a flat-plate SAH. A SAH system is a multi-variable system that is hard to model by conventional methods. As regards the ANN and WNN methods, it has a superior capability for generalization, and this capability is independent on the dimensionality of the input data’s. In this study, an ANN and WNN based methods were intended to adopt SAH system for efficient modelling. To evaluate prediction capabilities of different types of neural network models (ANN and WNN), their best architecture and effective training parameters should be found. The performance of the proposed methodology was evaluated by using several statistical validation parameters. Comparison between predicted and experimental results indicates that the proposed WNN model can be used for estimating the some parameters of SAHs with reasonable accuracy.