Complex Neuro-fuzzy System Research Articles

Complex Neurofuzzy ARIMA Forecasting—A New Approach Using Complex Fuzzy Sets

A novel complex neurofuzzy autoregressive integrated moving average (ARIMA) computing approach is presented for the problem of time-series forecasting. The proposed approach integrates a complex neurofuzzy system (CNFS) using complex fuzzy sets (CFSs) and ARIMA models to form the proposed computing model, which is called the CNFS-ARIMA. The output of CNFS-ARIMA is complex-valued, of which the real and imaginary parts can be used for two different functional mappings. This is the so-called dual-output property. There is no fuzzy If-Then rule in the genesis of CNFS-ARIMA. For the formation of CNFS-ARIMA, structure learning and parameter learning are involved to self-organize and self-tune the CNFS-ARIMA. A class of CFSs is used to describe the premise parts of fuzzy If-Then rules, whose consequent parts are specified by ARIMA models. CFS is an advanced fuzzy set whose membership degrees are complex-valued within the unit disc of the complex plane. With the synergetic merits of CNFS and ARIMA, CNFS-ARIMA models have excellent nonlinear mapping capability for time-series forecasting. A number of benchmark time series are used to test the proposed approach, whose results are compared with those by other approaches. Moreover, real-world financial time series, such as the National Association of Securities Dealers Automated Quotation (NASDAQ), the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX), and the Dow Jones Industrial (DJI) Average Index, are used for the proposed approach to perform the dual-output forecasting experiments. The experimental results indicate that the proposed approach shows excellent performance.

Intelligent financial time series forecasting: A complex neuro-fuzzy approach with multi-swarm intelligence

Financial investors often face an urgent need to predict the future. Accurate forecasting may allow investors to be aware of changes in financial markets in the future, so that they can reduce the risk of investment. In this paper, we present an intelligent computing paradigm, called the Complex Neuro-Fuzzy System (CNFS), applied to the problem of financial time series forecasting. The CNFS is an adaptive system, which is designed using Complex Fuzzy Sets (CFSs) whose membership functions are complex-valued and characterized within the unit disc of the complex plane. The application of CFSs to the CNFS can augment the adaptive capability of nonlinear functional mapping, which is valuable for nonlinear forecasting. Moreover, to optimize the CNFS for accurate forecasting, we devised a new hybrid learning method, called the HMSPSO-RLSE, which integrates in a hybrid way the so-called Hierarchical Multi-Swarm PSO (HMSPSO) and the wellknown Recursive Least Squares Estimator (RLSE). Three examples of financial time series are used to test the proposed approach, whose experimental results outperform those of other methods.

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.

Self-learning complex neuro-fuzzy system with complex fuzzy sets and its application to adaptive image noise canceling

In this paper we propose a new self-learning complex neuro-fuzzy system (CNFS) using complex fuzzy sets (CFSs). We design a class of Gaussian complex fuzzy sets for the proposed approach. This new computing model is applied to the problem of adaptive image noise canceling (AINC), where images are corrupted additively by unknown noise and the proposed CNFS is used to adaptively perform the task of image restoration. The proposed CNFS is focused on improving image quality with additive noise. The knowledge base of CNFS is composed of Takagi–Sugeno fuzzy If–Then rules, whose premises are described by CFSs. CFS is an advanced fuzzy set, which is described by a complex-valued membership function in the unit disc of the complex plane. The utility of CFSs can enhance the non-linear functional mapping ability of the CNFS, because CFSs can carry more information into fuzzy inference computing as well as more degrees of freedom for the adaption flexibility of CNFS. For optimal estimation of the parameters of CNFS, we devise a hybrid PSO–RLSE optimization method, which combines the well-known particle swarm optimization (PSO) method and the famous recursive least squares estimation (RLSE) method. Iteratively, the PSO is used to evolve the premise parameters of CNFS, based on which the RLSE is used to update the consequent parameters. The PSO–RLSE method is very efficient for fast learning. For AINC application, the proposed CNFS is used to mimic the behavior of unknown noise channel, so that corrupted images may be adaptively restored as close to its original version as possible. Several images are used to test the proposed approach, whose experimental results are compared to other approaches. The experimental results indicate that the proposed approach outperforms the compared approaches. Excellent performance for image restoration by the proposed approach has been observed.

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.

Complex fuzzy model with PSO-RLSE hybrid learning approach to function approximation

A new neuro-fuzzy computing paradigm using complex fuzzy sets to the problem of function approximation is proposed in this paper. The concept of complex fuzzy sets is an extension of traditional fuzzy set whose membership degrees are within a unit disc in the complex plane. The proposed complex system has excellent input-output mapping ability. To update the free parameters of the proposed complex neuro-fuzzy system (CNFS), a novel hybrid learning method is devised, combining both the well-known particle swarm optimisation (PSO) algorithm and the recursive least squares estimator (RLSE) algorithm. By the PSO-RLSE hybrid learning method, fast learning convergence is observed and better performance in accuracy is shown. To test the proposed approach, two benchmark functions are used. The experimental results by the proposed approach are compared to its neuro-fuzzy counterpart and to other approaches in literature. According to the experiment results, excellent performance by the proposed approach has been exposed.

Neuro-fuzzy system with high-speed low-power analog blocks

This paper proposes several improved CMOS analog circuits for neuro-fuzzy system, including Gaussian-like membership function circuit, minimization circuit, and a centroid algorithm defuzzification circuit not using division. A two-input/one-output neuro-fuzzy system composed of these circuits is implemented and testified for non-linear function approximating. All the circuits have been fabricated in SMIC 0.18 - μ m Mixed-signal CMOS technology. Experiment results show that all the proposed circuits provide characteristics of high operation capacity, high speed, and simple structures. They are very suitable for rapid implementation of high-speed complex neuro-fuzzy system.