Volterra filter modeling of nonlinear discrete-time system using improved particle swarm optimization

This paper focuses on the identification problem of nonlinear discrete-time systems using Volterra filter series model. Generally, to update the kernels of Volterra model, the most commonly used method is the gradient adaptive algorithm. However, this method probably traps at the local minimum for searching parameter solutions. In this study, a new intelligence swarm computation of the global search is considered. We utilize an improved particle swarm optimization (IPSO) algorithm to design the Volterra kernel parameters. It is somewhat different from the original algorithm due to modifying its velocity updating formula and this can promote the algorithm?s searching ability for solving the optimization problem. Using the IPSO algorithm to minimize the mean square error (MSE) between the actual output and model output, the identification problem for nonlinear discrete-time systems can be fulfilled. Finally, two different kinds of examples are provided to demonstrate the efficiency of the proposed method. Moreover, some examinations including the Volterra model memory size and algorithm initial condition are further considered.     

Digital secure communication via chaotic systems

This paper presents a novel secure communication system for digital signal transmissions. It contains four important parts: modulation, chaotic transmitter, chaotic receiver, and demodulation. The modulation mechanism is to modulate each of delivered bit information to be a carrier signal in the continuous form. Then this carrier signal is taken as a parameter of the chaotic system, called the unified chaotic system. Such a system possesses three different types of chaos characterizations depending on its system's parameter, and this guarantees the communication security more. In the public channel, only three chaotic state variables are delivered and this means that the important carrier information is efficiently screened. According to chaotic states received in the receiver terminal, the continuous carrier signal is decrypted using certain adaptation mechanisms. Finally, the proposed demodulation method can successfully recover the original bit information which is embedded in the communication systems. Some simulation results are provided to verify the efficiency of the proposed secure communication system.     

Application of an auto-tuning neuron to sliding mode control

This paper presents a control strategy that incorporates an auto-tuning neuron into the sliding mode control (SMC) in order to eliminate the high control activity and chattering due to the SMC. The main difference between the auto-tuning neuron and the general one is that a modified hyperbolic tangent function with adjustable parameters is employed. In this proposed control structure, an auto-tuning neuron is then used as the neural controller without any connection weights.. The control law will be switched from the sliding control to the neural control, when the state trajectory of system enters in some boundary layer. In this way, the chattering phenomenon will not occur. The results of numerical simulations are provided to show the control performance of our proposed method.     

PID Controller Design for MIMO Processes Using Improved Particle Swarm Optimization

This paper aims at the PID control system design for multivariable input and multivariable output (MIMO) processes. An improved version of a particle swarm optimization (PSO) algorithm is utilized to design PID control gains in MIMO control systems. In addition to the individual best and the global best particles, the velocity updating formula of the developed algorithm includes a new factor, the best particle of each sub-population, to enhance the search capacity. Based on the improved particle swarm optimization (IPSO), a complete design strategy is proposed for MIMO PID control systems. All control gains will be evolved to the optimal values by minimizing the system performance criterion. To show the efficiency of the proposed design method, a multivariable chemical process system with two inputs and two outputs is illustrated. Some experiment results, including different algorithm parameter settings and comparisons with other methods, are given. Numerical simulations indicate that the proposed method is superior to other optimal methods.     

Two-dimensional fractional-order digital differentiator design by using differential evolution algorithm

Designing a fractional-order digital differentiator often requires considerably complex mathematical operations and numerical approximations. Thus this paper will propose a simple method to achieve the fractional-order digital differentiator design, particularly for two-dimensional fractional-order differentiators. A two-dimensional finite impulse response (FIR) digital filter structure is utilized and designed so that its corresponding magnitude response can satisfy that of a desired fractional-order differentiator of two variables. The algorithm used to design such two-dimensional digital differentiator is the differential evolution (DE), which is one of evolutionary computations and has excellent searching capacity. The efficiency of the proposed scheme can be confirmed by some illustrative examples.     

Improved Representation-burden Conservation Network for Learning Non-stationary VQ

In a recent publication [1], it was shown that a biologically plausible RCN (Representation-burden Conservation Network) in which conservation is achieved by bounding the summed representation-burden of all neurons at constant 1, is effective in learning stationary vector quantization. Based on the conservation principle, a new approach for designing a dynamic RCN for processing both stationary and non-stationary inputs is introduced in this paper. We show that, in response to the input statistics changes, dynamic RCN improves its original counterpart in incremental learning capability as well as in self-organizing the network structure. Performance comparisons between dynamic RCN and other self-development models are also presented. Simulation results show that dynamic RCN is very effective in training a near-optimal vector quantizer in that it manages to keep a balance between the equiprobable and equidistortion criterion.     

Differential evolution-based nonlinear system modeling using a bilinear series model

This paper presents a new modeling method for nonlinear dynamic systems based on using bilinear series model. Basically, bilinear model is an extension of infinite impulse response (IIR) filter and belongs to the recursive nonlinear system model, i.e., its past output signals will heavily affect the present output. This kind of model can efficiently approximate a large class of nonlinear systems with fewer parameters than other non-recursive models. To adjust the model kernels, we here adopt an evolutionary computation called the differential evolution (DE) algorithm. This algorithm is based on real-valued manipulations and has a good convergence property for finding the global solution or the near global solution of optimized problem. Design steps of DE-based nonlinear system modeling are clearly given in this study. Finally, two kinds of digital systems are illustrated to demonstrate the efficiency of the proposed method.     

Gesture modeling for architectural design

This paper presents an intuitive method to simulate architectural space and objects by using gesture modeling. This method applies hand movements to retrieve data and generate objects under circumstances allowing larger tolerance. Communication between gesture (actor) and computer (listener) is conducted with a set of basic components like gestures, regulations, and interfaces. The components are used as theoretical models to assist gesture-aided 3-D modeling. Gesture is analyzed by both surface and deep structures in syntactic structure. At the outset, persons of different backgrounds were requested to model a virtual building. Several basic gesture types were concluded based on experiment categorization. The test result was used to build a theoretical model and conduct subsequent simulation. A 3-D digitizer associated with software is applied as a communication interface between user and computer. Gestures are simulated by moving a stylus held by one hand.     

A self-tuning PID control for a class of nonlinear systems based on the Lyapunov approach

In this paper, we will propose a self-tuning method for a class of nonlinear PID control systems based on Lyapunov approach. The three PID control gains are adjustable parameters and will be updated online with a stable adaptation mechanism such that the PID control law tracks certain feedback linearization control, which is previously designed. The stability of closed-loop nonlinear PID control system is analyzed and guaranteed by introducing a supervisory control and a modified adaptation law with projection. Finally, a tracking control of an inverted pendulum system is illustrated to demonstrate the control performance by using the proposed method.     

On the Characteristics of Growing Cell Structures (GCS) Neural Network

In this paper, a self-developing neural network model, namely the Growing Cell Structures (GCS) is characterized. In GCS each node (or cell) is associated with a local resource counter (t). We show that GCS has the conservation property by which the summation of all resource counters always equals , thereby s is the increment added to (t) of the wining node after each input presentation and (0 < < 1.0) is the forgetting (i.e., decay) factor applied to (t) of non-wining nodes. The conservation property provides an insight into how GCS can maximize information entropy. The property is also employed to unveil the chain-reaction effect and race-condition which can greatly influence the performance of GCS. We show that GCS can perform better in terms of equi-probable criterion if the resource counters are decayed by a smaller .