A hybrid evolutionary neuro-fuzzy system (HENFS) is proposed in this paper, where the weighted Gaussian function (WGF) is used as the membership function for improved premise construction. With the WGF, different types of the membership functions (MFs) can be accommodated in the rule base of HENFS. A new hybrid algorithm of random optimization (RO) algorithm incorporated with the least square estimation (LSE) is presented. Based on the hybridization of RO-LSE, the proposed soft-computing approach overcomes the disadvantages of other widely used algorithms. The proposed HENFS is applied to chaos time series identification and industrial process modeling to verify its feasibility. Through the illustrations and comparisons the impressive performances for unknown system identification can be observed.

In this paper, we present the method for identifying an Adaptive Neuro-Fuzzy Networks (ANFN) with Takagi-Sugeno-Kang (TSK) fuzzy type based on fuzzy granulation. We also develop a systematic approach to generating fuzzy if-then rules from a given input-output data. The proposed ANFN is designed by the use of fuzzy granulation realized via context-based fuzzy clustering. This clustering technique builds information granules in the form of fuzzy sets and develops clusters by preserving the homogeneity of the clustered patterns associated with the input and output space. The experimental results reveal that the proposed model yields a better performance in comparison with Linguistic Models (LM) and Radial Basis Function Networks (RBFN) based on context-based fuzzy clustering introduced in the previous literature for Box-Jenkins gas furnace data and automobile MPG prediction.

This paper presents two new intelligent methods to linearize the Multi-Carrier Power Amplifiers (MCPA). One of the them is based on the Neuro-Fuzzy controller while the other uses two small neural networks as a polar predistorter. Neuro-Fuzzy controllers are not model based, and hence, have ability to control the nonlinear systems with undetermined parameters. Both methods are adaptive, low complex, and can be implemented in base-band part of the communication systems. The performance of the linearizers is obtained via simulation. The simulation is performed for three different scenarios; namely, a multi-carrier amplifier for GSM with four channels, a CDMA amplifier and a multi-carrier amplifier with two tones. The simulation results show that Neuro-Fuzzy Controller (NFC) and Neural Network Polar Predistorter (NNPP) have higher efficiencies so that reduce IMD3 by more than 42 and 32 dB, respectively. The practical implementation aspects of these methods are also discussed in this paper.

Recognition of specified wave patterns in one-dimensional signals is an important task in many application areas such as computer science, medical science, and geophysics. Many researchers have tried to automate this task with various techniques, recently the soft computing algorithms. This paper proposes a new neuro-fuzzy recognition system for detecting one-dimensional wave patterns using wavelet coefficients as features of the signals and evolution strategy as the training algorithm of the system. The neuro-fuzzy recognition system first trains the wavelet coefficients of the training wave patterns and then evaluates the degree of matching between test wave patterns and the training wave patterns. This system was applied to picking first arrival events in seismic data. Experimental results with three seismic data showed that the system was very successful in terms of learning speed and performances.

In two- or more-dimensional systems where the components of the sample data are strongly correlated, it is not proper to divide the input space into several subspaces without considering the correlation. In this paper, we propose the usage of the method of principal component in order to uncorrelate and remove any redundancy from the input space of the adaptive neuro-fuzzy inference system (ANFIS). This leads to an effective partition of the input space to the fuzzy model and significantly reduces the modeling error. A computer simulation for two frequently used benchmark problems shows that ANFIS with the uncorrelation process performs better than the original ANFIS under the same conditions.

In this paper, a new digital chaos circuit which can generate multiple-scroll strange attractors is proposed. Being based on the piecewise-linear function which is determined by on-chip supervised learning, the proposed digital chaos circuit can generate multiple-scroll strange attractors. Hence, the proposed circuit can exhibit various bifurcation phenomena. By numerical simulations, the learning dynamics and the quasi-chaos generation of the proposed digital chaos circuit are analyzed in detail. Furthermore, as a design example of the integrated digital chaos circuit, the proposed circuit realizing the nonlinear function with five breakpoints is implemented onto the FPGA (Field Programmable Gate Array). The synthesized FPGA circuit which can generate n-scroll strange attractors (n=1, 2, 4) showed that the proposed circuit is implementable onto a single FPGA except for the SRAM.

In this paper, an FPGA (Field Programmable Gate Array)-implementable digital chaos circuit with nonlinear mapping function learning ablility is proposed. The features of this circuit are user-programmability of the mapping functions by on-chip supervised learning, robustness of chaos signal generation based on digital processing, and high-speed and low-cost thanks to its FPGA implementation. The circuit design and analysis are presented in detail. The learning dynamics of the circuit and the quantitization effect to the quasi-chaos generation are analyzed by numerical simulations. The proposed circuit is designed by using an FPGA CAD tool, Verilog-HDL. This confirmed that the one-dimensional chaos circuit block (except for SRAM's) is implementable on a single FPGA chip and can generate quasi-chaos signals in real time.

A new current-mode sampled-data chaos circuit is proposed. The proposed circuit is composed of an operation block, a parameter block, and a delay block. The nonlinear mapping functions of this circuit are generated in the neuro-fuzzy based operation block. And these functions are determined by supervised learning. For the proposed circut, the dynamics of the learning and the state of the chaos are analyzed by computer simulations. The design conditions concerning the bifurcation diagram and the nonlinear mapping function are presented to clarify the chaos generating conditions and the effect of nonidealities of the proposed circuit. The simulation results showed that the nonlinear mapping functions can be realized with the precision of the order of several percent and that different kinds of bifurcation modes can be generated easily.