МОДЕЛИРОВАНИЕ УГРОЗ БЕЗОПАСНОСТИ ИНФОРМАЦИИ В ТЕРРИТОРИАЛЬНО-РАСПРЕДЕЛЕННЫХ ИНФОРМАЦИОННЫХ СИСТЕМАХ

This paper first introduces the algorithm of Kalman wave.Because random disturbance noise is uncertain, unpredictable, and the mean and variance of noise, for this kind of interference, taking maximum a posteriori (MAP) noise estimator is Sage -Husa to estimate the disturbance of the system noise and measurement noise mean and variance, to realize the Kalman filter for uncertain signal suppression.Then the adaptive Kalman filtering algorithm is combined with fuzzy PID control algorithm, and it is proposed based on adaptive Kalman filter model PID control algorithm, which has both fuzzy PID adaptive control system parameters, fast tracking performance, but also it has the adaptive Kalman filtering jamming signal inhibitory effect of uncertainty.At last, through simulation experiment, results show that the fuzzy PID control based on adaptive Kalman filtering algorithm with fast response speed, high precision, good robustness, and can effectively overcome all kinds of jamming noise existing in the system.

A hybrid fuzzy neural architecture is proposed. The fuzzy neural system is a feedforward network that combines the basic notions of neural networks and fuzzy logic into a common structure. A back-propagation algorithm is used to train the FNS to perform the desired nonlinear mappings. Four simulation examples are presented where the fuzzy neural system is employed as a nonlinear identifier. Finally, comparisons between fuzzy neural systems, backpropagation neural networks and other fuzzy systems are given and discussed.

In this study, using a model reference adaptation law, a stable fuzzy neural control system is developed. Despite the advantages of Model reference control design technique, which is mainly its power to exactly set trajectories of the system under control, this method is designed for linear system. In this study using fuzzy neural systems, a stable model reference controller for nonlinear systems is developed, Lyapunov method is used to guarantee the stability of fuzzy neural training algorithm and model following of the system under control.

Research on A Fuzzy Adaptive PID Control Method for Four Rotor UAV Control System With the continuous improvement of national living standards, people have higher and higher requirements for the reliability of power supply. The emergence of unmanned aerial vehicle inspection technology provides a new idea to solve the contradiction between the increasing equipment scale and the increasing inspection requirements. However, at present, the stability of the traditional proportion integration differentiation (PID) control system of the four rotor unmanned aerial vehicle (UAV) is not high enough. During the inspection, there are accidents of drones hitting power equipment from time to time. In view of the above problems, this paper studies the fuzzy adaptive PID control method to support the stable flight of four rotor UAV. The principle of fuzzy control is analyzed. The fuzzy control method and PID control method are deeply integrated. A fuzzy adaptive PID control system is designed. The universe and membership function of the relevant control parameters of the system are formulated. The tuning rules of fuzzy adaptive PID control system are formulated. Finally, the improved control system is simulated. The simulation results show that the fuzzy adaptive PID control algorithm can effectively improve the stability, reliability and emergency ability of UAV. This method can effectively improve the adaptability of UAV to perform complex missions in complex environments. The research results have guiding significance for expanding the application field of UAV.

In this paper the identification and control of dynamic plants using type-2 TSK fuzzy neural system (FNS) is considered. The systems constructed on the base of type-1 fuzzy systems cannot directly handle the uncertainties associated with information or data in the knowledge base of the process. One possible way to alleviate the problem is to resort to the use of type-2 fuzzy systems. In this paper, a type-2 TSK fuzzy neural system (FNS), is proposed and its gradient learning algorithm is derived. Its performance for identification and control of time-varying plants is evaluated and compared with other approaches seen in the literature; the time-varying nature of the plants being handled as uncertainties in the plant coefficients which can be described by type-2 fuzzy sets.

The load prediction of electric vehicles (EVs) is the basis for the planning of distribution network facilities. The assumptions of the charging conditions of EVs are not reasonable enough in the previous research. Aiming at this problem, this paper proposes a novel method to predict the distribution of EV charging load in time and space based on fuzzy inference algorithm. First, compared with the previous research, a trip chain model is established to describe the dynamic process of EVs more comprehensively. Then, in view of the influence of traffic factors, we simulate the actual travel situation of EVs through the traffic congestion factor, and the probabilistic density functions of space-time variables in the trip chain are modeled. Next, considering three factors which affect the charging behavior of EVs, a three-input-one-output fuzzy inference system (FIS) is proposed to calculate the charging probability, instead of assuming that the charging behavior takes place at a certain time or satisfies a certain formula in the traditional method. Finally, the load distribution curves of EVs in different charging regions are obtained by Monte Carlo simulation. The simulation results verify the validity and accuracy of the proposed prediction method.

While artificial neural networks and fuzzy systems have both been used as universal approximators, the two approaches have different advantages. For example, neural networks are good at classification and learning, while fuzzy systems can perform inference. To take advantage of such complementary strengths, various hybrid neural-fuzzy systems have been devised. The research reported here involves a new combination of neural and fuzzy systems developed for the protein folding problem, that is, how to estimate the number of topological hydrophobic contacts in the (unknown) most stable conformation of a given sequence of monomer residues. Fuzzy meta-rules are used to generate a series of neural networks for longer and longer input monomer sequences. >

Serving is the most important hitting technique in tennis, and a good service receiving can instantly reverse the active and passive relationship between serve and receive on the tennis court, and control the rhythm of the court. The purpose of this study is to use an adaptive neuron fuzzy intelligent system to analyze some techniques of tennis serve. In this study, eight male players from the school tennis team were selected as the experimental subjects, whose sports level was above the national tennis level II. Ten weeks before the simulation test, the training time and frequency of 8 subjects were the same. In other words, 5 times a week, 2.5 hours±0.5 hours. The work engineering of adaptive fuzzy system firstly, in the off-line modeling stage, the adaptive fuzzy system uses the rule self splitting technology to generate the initial fuzzy rules, and uses the improved adaptive neural network algorithm to optimize the calculation; then according to the error between the system input and the predicted output, the independent variable is adjusted and replaced; at the same time, the adaptive fuzzy system is further used for calculation In the process of tennis serving, the nonlinear control variables are obtained online and applied to the fuzzy system for control. Next, in the experiment, the system was used to record the body’s movement and service scores during service. The experimental results show that during the service process, the maximum trunk torsion amplitude can reach 48.26 ° and the minimum is only 5.41 ° and the service score accounts for 81.41% and 80.47% of the total scores of the two sections respectively. This shows that the fuzzy system in this study can effectively analyze the service posture and score of athletes. It is concluded that the accurate calculation and analysis of tennis serve by adaptive neuron intelligent fuzzy system in this study is conducive to improve the tennis serviceability and competition performance of players. This research has made a certain contribution to the intellectualization of sports.

This paper describes the implementation of a direct adaptive control of a nonlinear underactuated mechatronics system known as the Pendubot robot using fuzzy systems and neural networks. A PD fuzzy controller is employed to control the two links motion from the free hanging position to the vertical position (the swing-up controller). Then, an intelligent adaptive fuzzy radial Gaussian neural networks system is used to control the Pendubot at the vertical position (the balancing controller) by five rules only in case of parameters uncertainty. This algorithm is proven to be globally stable, with errors converging to a neighbourhood of zero. Finally, the simulation results confirm the theoretic analysis

Investors react to news, particularly to earnings and dividend announcements released by respective firms, and consequently stock prices move. Thus, news has an impact on stock prices. However, the price adjustment process is a complex one. While neural fuzzy systems have advantages over statistical methods in modeling and predicting complex relationships generally, not many neural fuzzy systems share the same level of competence and capabilities. In this study, we evaluate the effectiveness of four neural fuzzy systems - feed forward neural network (FFNN), adaptive neuro fuzzy inference system (ANFIS), radial basis function network (RBFN) and rough set based pseudo outer product rule (RSPOP), respectively - in predicting the impact of news on stock price movements. We found that rough set based pseudo outer product rule (RSPOP) is the most effective system in the study undertaken, and is a candidate for further evaluation as a financial intelligence system.

Studies on interval type-2 TSK fuzzy logic system is a hot topic in the current academic area. Parameter identification is very important for system design. Commonly used parameter identification methods are the least-square algorithm, BP algorithm, etc., few scholars use QPSO algorithm for parameter identification. In this paper, we propose a design of interval type-2 TSK fuzzy logic system based on quantum behaved particle swarm optimization (QPSO) intelligent algorithm. Firstly, by combining the A1–C1, A2–C0, A2–C1 interval type-2 TSK fuzzy logic system with neural network, the fuzzy neural network system is designed. Then, the QPSO intelligent algorithm was used to tune the fuzzy neural network system parameters, and the designed system model is applied to predict the Nasdaq Composite Index and International Gold Prices. Both QPSO algorithm and BP algorithm have been used to train the system model. By comparing QPSO algorithm and BP algorithm, the performance index and simulation results illustrated the proposed model is effective and feasible, which can achieve a better performance. Finally, compare the performance of the four fuzzy logic systems, it can be seen the effect of A2–C1 fuzzy logic system is better than that of the other three fuzzy logic systems.

This chapter shows how neural fuzzy systems can improve motion estimation and compensation for video compression. Motion estimation and compensation are key parts of video compression. They help remove temporal redundancies in images. Most motion estimation algorithms neglect the strong-temporal correlations within the motion field. The search windows stay the same through the image sequences and the estimation needs heavy computation. We use an unsupervised neural vector quantizer system can use the temporal correlation of the motion field to estimate the motion vectors. First-order and second-order statistics of the motion vectors give ellipsoidal search windows. This algorithm reduces the search area and entropy and gives clustered motion fields. Motion-compensated video coding further assumes that each block of pixels moves with uniform translational motion. This often does not hold and can produce block artifacts. We use a supervised neural fuzzy system to compensate for the overlapped block motion. This fuzzy system uses the motion vectors of neighboring blocks to map the prior frame’s pixel values to the current pixel value. The neural fuzzy system used 196 rules that came from the prior decoded frame. The fuzzy system learns and updates its rules as it decodes the image. The fuzzy system also improved the compensation accuracy. The appendix derives both the fuzzy system and the supervised neural-like learning laws that tune its parameters.

Automatic generation of fuzzy rule base and membership functions from an input-output data set, for reliable construction of an adaptive fuzzy inference system, has become an important area of research interest. We propose a new robust, fast acting adaptive fuzzy pattern classification scheme, named influential rule search scheme (IRSS). In IRSS, rules which are most influential in contributing to the error produced by the adaptive fuzzy system are identified at the end of each epoch and subsequently modified for satisfactory performance. This fuzzy rule base adjustment scheme is accompanied by an output membership function adaptation scheme for fine tuning the fuzzy system architecture. This iterative method has shown a relatively high speed of convergence. Performance of the proposed IRSS is compared with other existing pattern classification schemes by implementing it for Fisher's iris data problem and Wisconsin breast cancer data problems.

Theory of fuzzy systems

Transferable representation modelling for real-time energy management of the plug-in hybrid vehicle based on k-fold fuzzy learning and Gaussian process regression