59 results about "Chaotic neural network" patented technology

The invention relates to a chaotic neural network encryption communication circuit, in particular to an encryption communication circuit based on a chaotic neuron network with a time delay state. A clear text signal i(t) of a transmission end drives a first time delay chaotic neural network system through a reversed phase amplification circuit, the first time delay chaotic neural network system outputs a chaotic signal x(t), the chaotic signal x(t) and the clear text signal i(t) are overlapped to generate a signal x(t)+i(t), an encryption transmission signal s(t) is generated through an encryption scheme circuit; and the encryption transmission signal s(t) is transmitted to a receiving end through a transmission channel, the signal x(t)+i(t) is solved through corresponding decryption scheme circuit to drive a second time delay chaotic neural network system, the second time delay chaotic neural network system generates a corresponding chaotic signal y(t) synchronous with the chaotic signal x(t), and the signal x(t)+i(t) is subtracted from the chaotic signal y(t) to obtain a clear text signal r(t). The chaotic neural network encryption communication circuit overcomes the defects that confidentiality of a common low-dimensional chaotic system is poor and a high-dimensional chaotic system is difficult to be physically implemented, implements encryption transmission function of theclear text signal, and effectively simplifies a real circuit device.

The invention provides an analysis method for infant brain medical computer scanning images and a realization system. The analysis method is to process the brain medical computer scanning images into images with prominent fractal characteristics, and then perform mathematic quantitative analysis on the images by a chaos fractal analysis method. The method is realized under Matlab R2007 through programming, utilizes chaotic neural network and fractal principles, performs serialized division, identification and fractal mode analysis on the infant brain medical computer scanning images to obtain fractal dimension quantified reference values and quantified reference values of multifractal spectrum width of normal infant brain medical computer scanning images in different age brackets, realizes clinical prediction on sick infants with intelligent disability and brain paralysis which have no typical neural image manifest characteristics by taking the reference values as a comparison standard, thoroughly changes the conventional analysis mode for the infant brain medical computer scanning images, and has high accuracy and strong repeatability compared with scoring results of the clinically widely used Gesell scale.

The invention discloses a radar multi-target tracking optimization method based on the chaotic neural network. The method is mainly characterized in that state one-step prediction of a tth target at the k time, measurement prediction of the tth target at the k time, the measurement prediction information of a j'th measurement for the tth target at the k time, a one-step prediction error covariance matrix of the tth target at the k time, an information covariance matrix of the tth target at the k time, Kalman gain of the tth target at the k time, an nk*T'-dimension measurement-target association matrix at the k time, an (nk+1)*T'-dimension effective likelihood function matrix of association of nk meausurements and T' targets at the k time, an (nk+1)*T'-dimension normalization matrix of association of the nk meausurements and the T' targets at the k time, an (nk+1)*T'-dimension accurate probability matrix of association of the nk meausurements and the T' targets at the k time, a state equation of the tth target at the k time and an error covariance matrix of the tth target at the k time are sequentially calculated; the t is respectively made to be 1 to T', the error covariance matrix of the T'th targets at the k time is acquired, and real-time tracking for the T'th targets is carried out by a radar according to the error covariance matrix of the T'th targets at the k time.

The invention provides a signal blind detection method based on a complex sinusoidal chaotic neural network. A non-monotone excitation function combined by a complex sinusoidal chaotic neural network and Sigmoid, time-varying output function gains and a piecewise exponential annealing function are adopted to form a complex sinusoidal chaotic neural network; in case of each iteration, a chaotic neural network is entered firstly, and an activation function is then entered; being trapped in the local minimum can be prevented by the chaotic neural network. The method of the invention inherits the features of the chaotic neural network, and the blind detection performance is improved; the network has richer and more flexible transient chaos dynamics characteristics and stronger global searching capability; and in the same condition, the anti-noise performance is better than that of the traditional Hopfield signal blind detection algorithm.

The invention provides a signal blind detection method based on a double Sigmoid chaotic neural network. According to the method, by means of the chaotic neural network and a second activation function, the double Sigmoid chaotic neural network is formed, each time iteration is carried out, the chaotic neural network is logged in firstly and then the second activation function is logged in. Due to the fact that the chaotic neural network has the advantage of being capable of avoiding being stuck in the local minimum, blind detection performance is improved, anti-noise performance of the network operation speed is improved, and the method is superior to a traditional Hopfield signal blind detection algorithm.

The invention discloses an intelligent diagnosis system for a power supply and a protection method. Mainly, a failure of the power supply system is judged by the following two manners: 1) judging whether the power supply system works normally by using an infrared signal spectrum and a sound spectrum, 2) finding all the potential failure modes through a failure model and impact analysis based on apower supply circuit model, performing simulation operation under all the failure modes and obtaining simulation data so that the simulation data is more targeted and representative; and constructingthe power supply failure model in a manner of training a chaotic neural network through the simulation data. The power supply failure information obtained through the two manners not only contains thepower supply failure type but also contains an integrated chip or an electronic element, which breaks down in the power supply circuit, thereby greatly facilitating the subsequent inspection and maintenance of the worker, reducing the workload, and improving the working efficiency.