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Lorenz systems family unifying Lorenz system, Chen system and Lu system is a typical chaotic family. In this paper, we consider impulsive control Lorenz chaotic systems family with time-varying impulse intervals. By establishing an effective tool of a set of inequalities, we analyze the asymptotic stability of impulsive control Lorenz systems family and obtain some new less conservative conditions. Based on the stability analysis, we design a novel impulsive controller with time-varying impulse intervals. Illustrative examples are provided to show the feasibility and effectiveness of our method. The obtained results not only can be used to design impulsive control for Lorenz systems family, but also can be extended to other chaotic systems.

A whole impulsive control scheme of nonlinear systems with time-varying delays, which is an extension for impulsive control of nonlinear systems without time delay, is presented in this paper. Utilizing the Lyapunov functions and the impulsive-type comparison principles, we establish a series of different conditions under which impulsively controlled nonlinear systems with time-varying delays are asymptotically stable. Then we estimate upper bounds of impulse interval and time-varying delays for asymptotically stable control. Finally a numerical example is given to illustrate the effectiveness of the method.

This paper deals with the exponential stability of impulsive Takagi–Sugeno fuzzy systems with delay. Impulsive control and delayed fuzzy control are applied to the system, and the criterion on exponential stability expressed in terms of linear matrix inequalities (LMIs) is presented.

In this paper, the layer-by-layer optimizing algorithm for training multilayer neural network is extended for the case of a multilayer neural network whose inputs, weights, and activation functions are all complex. The updating of the weights of each layer in the network is based on the recursive least squares method. The performance of the proposed algorithm is demonstrated with application in adaptive complex communication channel equalization.

In this paper, global asymptotic stability for cellular neural networks with time delay is discussed using a novel Liapunov function. Some novel sufficient conditions for global asymptotic stability are obtained. Those results are simple and practical than those given by P. P. Civalleri, et al., and have a leading importance to design cellular neural networks with time delay.

By use of Hopfield model and basis solution of homogeneous linear equations which are established in accordance with consistent state, a practical decision method for the existence of optimal Hopfield model of combinational circuits is provided. Finally, an example is given.

A Stream-cipher Slow-frequency-hopping Communication System is proposed, where the stream-cipher sequence is used not only to encrypt the information data, but also to generate frequency-hopping code, thus the stream-cipher slow-frequency-hopping communication system combines the merit of stream-cipher technology and the merit of frequency-hopping technology; a stream-cipher synchronization protocol based on timing is designed; simulation results demonstrated that the principle of the stream-cipher slow-frequency-hopping communication system is correct.

A new method for analyzing dynamics of continuous neural networks is proposed, and the necessary convergence conditions for a class of associative networks are obtained. Based on the stability criterion and the equations of equilibrium set of the network, synthesis of a class of associative neural networks is given. The stability control model of asymmetric unstable networks is suggested, which is also a valid way for optimization and dynamic control of stable neural networks.

A new method for analyzing the stabilities of analog electronic neural networks is presented. The energy functions with clear physical meaning are derived by introducing the static equivalent circuit models, which has expanded the Tellegen Theorem for application on circuit analysis. The method used to derive the energy functions of nets from first order differential equations is valid for all first order continuous autonomous systems. The stability analysis of cellular neural networks is made by the use of the stationary cocontent theorem. Some results are instructive for the network implementation on circuits.

In this paper, we analyze the ACS specification and the reasons for 33dB in WCDMA systems, which is the minimal required value of ACS. The tests of the practical RF receive circuits is done under the condition that the 3 center frequencies are 2110 MHz, 2140 MHz and 2170 MHz respectively. The result is the level of the modulated interference signal is between -48 dBm and -42 dBm @ BER = 0.001, and the amplitude margin is between 3.9 dB and 9.2 dB, when the frequency offset from carrier is /spl plusmn/ 5 MHz. Obviously, it satisfies the -52 dBm requirements of 3GPP.

In this paper, an application of genetic algorithm with guiding strategy in the design of a stable 1D IIR digital filter is presented. The difference between traditional GA (genetic algorithm) and our modified GA is given in detail. A stable 1D IIR filter design example is given to justify the effectiveness and advantages of the proposed design method.

Spectral regrowth of a code division multiple access (CDMA) signal passed through a weakly nonlinear circuit are analyzed using power series and statistical method The method yields an analytical expression for the autocorrelation function of the output signal as a the statistical function of the input signal transformed by a behavioral model of the amplifier. It provides helpful predictions of inter-modulation in wireless communication systems with nonlinear distortion effects. This method for the estimation of spectral regrowth for a CDMA signal passed through a nonlinear power circuit have been used in the design of a transceiver for CDMA communication systems.

Starting with an investigation of the Haar scaling function, we introduce the scaling coefficient function. Then the Battle-Lemarie bases are extended to the generalized Battle-Lemarie bases by introducing a time-shift factor into the Battle-Lemarie scaling coefficient functions. The rationality of the presented extended method is theoretically proved, and some basic properties of the generalized Battle-Lemarie bases, such as orthonormality, time frequency localization, exponential decay features, and so on, are discussed. Meanwhile a series of neoteric symmetric multiresolution analysis bases is generated. The generalized Battle-Lemarie wavelets are better than the corresponding original Battle-Lemarie wavelets in regularity.

As more datapath stacks are integrated into system-on-chips (SOC), the datapath is becoming a critical part of the whole giga-scale integrated circuit (GSI) design. The traditional layout design methodology cannot satisfy the datapath performance requirements since it has no knowledge of the datapath bit-sliced structure. In this paper, we applied an approach of OpenTS (open tabu search) to determine the optimal datapath element ordering to minimize the track density, the wire length and interconnection delay. Experimental results show that the OpenTS is more efficient than the existing genetic and simulated annealing approaches.

With the high demand for reliability and performance, it is essential to determine an accurate estimation of maximum CMOS combinational circuit power consumption. However, for large-scaled circuits, the problem of determining the input patterns to induce maximum current and the maximum power is NP-complete. In this paper, a novel approach was proposed to obtain a lower bound of the maximum power consumption using a THGA (tabu-hierarchy genetic algorithm). The experiments with ISCAS85 benchmark circuits show that this approach generates the lower bound with a quality that cannot be achieved using simulation-based techniques and other methods.

A new blind identification method based on signal subspace estimation is proposed. With the dispersive CDMA channel modeled as a multiple-input multiple-output signal model, the impulse response of the multiuser's channels can be identified without training sequences. The method characterizes parallel processing and lower algorithmic complexity, hence it is suitable for high-rate CDMA communication system. The computational requirement of the algorithm is o(N/sup 2/), N being the size of the covariance matrix. The algorithm is locally convergent and the undesired stationary points are unstable. Monte-Carlo simulations demonstrate the performance of the proposed methods in a channel identification context.