Chunshien Li

Fuzzy–Neural Sliding-Mode Control for DC–DC Converters Using Asymmetric Gaussian Membership Functions

A fuzzy-neural sliding-mode (FNSM) control system is developed to control power electronic converters. The FNSM control system comprises a neural controller and a compensation controller. In the neural controller, an asymmetric fuzzy neural network is utilized to mimic an ideal controller. The compensation controller is designed to compensate for the approximation error between the neural controller and the ideal controller. An online training methodology is developed in the Lyapunov sense; thus, the stability of the control system can be guaranteed. Finally, to investigate the effectiveness of the FNSM control scheme, it is applied to control a pulsewidth-modulation-based forward dc-dc converter. Experimental results show that the proposed FNSM control system is found to achieve favorable regulation performances even under input-voltage and load-resistance variations

Self-organizing neuro-fuzzy system for control of unknown plants

A cluster-based self-organizing neuro-fuzzy system (SO-NFS) is proposed for control of unknown plants. The neuro-fuzzy system can learn its knowledge base from input-output training data. A plant model is not required for training, that is, the plant is unknown to the SO-NFS. Using new data types, the vectors and matrices, a construction theory is developed for the organization process and the inference activities of the cluster-based SO-NFS. With the construction theory, a compact equation for describing the relation between the input base variables and inference results is established. This equation not only gives the inference relation between inputs and outputs but also specifies the linguistic meanings in the process. New pseudo-error learning control is proposed for closed-loop control applications. Using a cluster-based algorithm, the neuro-fuzzy system in its genesis can be generated by the stimulation of input/output training data to have its initial control policy (IF-THEN rules) for application. With the well-known random optimization method, the generated neuro-fuzzy system can learn its data base for specific applications. The proposed approach can be applied on control of unknown plants, and can levitate the curse of dimensionality in traditional fuzzy systems. Two examples are demonstrated.

Recurrent neuro-fuzzy hybrid-learning approach to accurate system modeling

A recurrent neuro-fuzzy approach with RO-LSE hybrid learning algorithm to the problem of system modeling is proposed in the paper. The proposed recurrent neuro-fuzzy system possesses six layers of neural network to perform the fuzzy inference. The recurrent structure is formed using lagged membership-grade signals as internal feedbacks to the layer of membership functions of fuzzy sets, and it is expected having great potential to trace the temporal change of signals. Fuzzy sets with time-varying kernels have excellent property, with that the input-output mapping of the neuro-fuzzy system is no longer fixed but time varying. In this study, a new parameter learning approach is proposed for NFS with good learning convergence, in which the hybrid RO-LSE learning algorithm is utilized for the update of parameters. The well-known random optimization (RO) method is used to update the parameters of the premise parts of the proposed system, and the method of least square estimation (LSE) to update those of the consequent parts. The hybrid algorithm is found useful, and it has shown fast convergence of parameter learning for the proposed system. Three examples are used to demonstrate the brilliancy of the proposed approach. Excellent performance of the proposed approach in modeling accuracy and learning convergence is observed.

Fuzzy motion control of an auto-warehousing crane system

Fuzzy motion control of an auto-warehousing crane system is presented in this paper. Using the concept of linguistic variable, a fuzzy logic controller (FLC) can convert the knowledge and experience of an expert into an automatic control strategy. The designed FLC with a rule base and three sets of parameters is used to control the crane system in x, y, and z directions. The unloaded weight and the fully loaded weight of the crane system in discussion are 1.35/spl times/10/sup 4/ kg and 1.5/spl times/10/sup 4/ kg, respectively. For various loading conditions and varying distances, the FLC still controls the crane system very well with positioning accuracy less than 2/spl times/10/sup -3/ m for all directions. The distance-speed reference curve for control of the crane system is designed to meet the engineering specifications of motion such as acceleration, deceleration, maximum speed, and creep speed in each direction, and is generated automatically according to varying distance. The method for designing the distance-speed reference curve can make the crane move at relatively high speed to approach the target position. Simulations of the motion control in the three directions are demonstrated.

Pseudoerror-based self-organizing neuro-fuzzy system

The novel concept of pseudoerrors for a self-organizing neuro-fuzzy system (SO-NFS) is proposed for tracking control problem. To demonstrate the proposed approach, an example of motion control of an auto-warehousing crane system is illustrated, which can move back and forth in x,y, and z directions to access and store cargoes. The proposed SO-NFS shows excellent performance in control of the crane system for different loading conditions and varying distances in all directions.

A novel self-organizing complex neuro-fuzzy approach to the problem of time series forecasting

A self-organizing complex neuro-fuzzy intelligent approach using complex fuzzy sets (CFSs) is presented in this paper for the problem of time series forecasting. CFS is an advanced fuzzy set whose membership function is characterized within a unit disc of the complex plane. With CFSs, the proposed complex neuro-fuzzy system (CNFS) that acts as a predictor has excellent adaptive ability. The design for the proposed predictor comprises the structure and parameter learning stages. For structure learning, the FCM-Based Splitting Algorithm for clustering was used to determine an appropriate number of fuzzy rules for the predictor. For parameter learning, we devised a learning method that integrates the method of particle swarm optimization and the recursive least squares estimator in a hybrid and cooperative way to optimize the predictor for accurate forecasting. Four examples were used to test the proposed approach whose performance was then compared to other approaches. The experimental results indicate that the proposed approach has shown very good performance and accurate forecasting.

Function Approximation with Complex Neuro-Fuzzy System Using Complex Fuzzy Sets – A New Approach

A new neuro-fuzzy computing paradigm using complex fuzzy sets is proposed in this paper. The novel computing paradigm is applied to the problem of function approximation to test its nonlinear mapping ability. A complex fuzzy set (CFS) is an extension of traditional type-1 fuzzy set whose membership is within the unit real-valued interval. For a CFS, the membership is extended to complex-valued state within the unit disc of the complex plane. For self-adaption of the proposed complex neuro-fuzzy system (CNFS), the Particle Swarm Optimization (PSO) algorithm and Recursive Least Squares Estimator (RLSE) algorithm are used in a hybrid way to adjust the free parameters of the CNFS. With the novel PSO-RLSE hybrid learning method, the CNFS parameters can be converged efficiently and quickly. By the PSO-RLSE method for the CNFS, fast learning convergence is observed and great performance in accuracy is shown. In the experimental results, the CNFS shows much better performance than its traditional neuro-fuzzy counterpart and other compared approaches. Excellent performance by the proposed approach has been shown.

Thin gold film strain gauges

Gold thin films, of thickness 30, 60, 100, and 300 A were studied for use as miniature strain gauges. The thin metal films were thermally evaporated onto silicon dioxide coated silicon wafers and patterned into strain gauges with dimensions of 100 μm×70 μm and annealed at a maximum temperature of 400 °C. The silicon substrate was cut into cantilever beams to calibrate the strain gauges by loading the beams. The impedance, Z, was measured over a frequency range from 5 kHz to 1 MHz. For the 30, 60, and 100 A thick films the magnitude of the impedance was typically 1 MΩ at 5 kHz and the gauge factor ([Δ‖Z‖/‖Z‖]/e) was 24–48 at small strain (e<2.8×10−6). The gauge factor was independent of frequency but decreased at larger strains. The 300 A thick films were typically 110Ω with a gauge factor of 2.6. The conduction process for the island‐like film was modeled with activated tunneling. The sensitivity [Δ‖Z‖/‖Z‖]/e versus strain response model included a contribution from the strain energy in the activated tunn...

A new ARIMA-based neuro-fuzzy approach and swarm intelligence for time series forecasting

Time series forecasting is an important and widely interesting topic in the research of system modeling. We propose a new computational intelligence approach to the problem of time series forecasting, using a neuro-fuzzy system (NFS) with auto-regressive integrated moving average (ARIMA) models and a novel hybrid learning method. The proposed intelligent system is denoted as the NFS-ARIMA model, which is used as an adaptive nonlinear predictor to the forecasting problem. For the NFS-ARIMA, the focus is on the design of fuzzy If-Then rules, where ARIMA models are embedded in the consequent parts of If-Then rules. For the hybrid learning method, the well-known particle swarm optimization (PSO) algorithm and the recursive least-squares estimator (RLSE) are combined together in a hybrid way so that they can update the free parameters of NFS-ARIMA efficiently. The PSO is used to update the If-part parameters of the proposed predictor, and the RLSE is used to adapt the Then-part parameters. With the hybrid PSO-RLSE learning method, the NFS-ARIMA predictor may converge in fast learning pace with admirable performance. Three examples are used to test the proposed approach for forecasting ability. The results by the proposed approach are compared to other approaches. The performance comparison shows that the proposed approach performs appreciably better than the compared approaches. Through the experimental results, the proposed approach has shown excellent prediction performance.

Complex-fuzzy adaptive image restoration: an artificial-bee-colony-based learning approach

A complex-fuzzy approach using complex fuzzy sets is proposed in the paper to deal with the problem of adaptive image noise cancelling. A image may be corrupted by noise, resulting in the degradation of valuable image information. Complex fuzzy set (CFS) is in contrast with traditional fuzzy set in membership description. A CFS has the membership state within the complexvalued unit disc of the complex plane. Based on the membership property of CFS, we design a complex neural fuzzy system (CNFS), so that the functional mapping ability by the CNFS can be augmented. A hybrid learning method is devised for training of the proposed CNFS, including the artificial bee colony (ABC) method and the recursive least square estimator (RLSE) algorithm. Two cases for image restoration are used to test the proposed approach. Experimental results are shown with good restoration quality.