54 results about "Ergodicity" patented technology

The invention pertains to a path testing in a program testing. The concept of a program control flow base path is defined through bringing in the concept of base in mathematics, a data structure showing a program structure of a source program slice is obtained by using a compiler module first; then through traversing the data structure, a control flow path generating algorithm is utilized to generate a subset compiler module of a program control flow path which is based on a base path to interpret the semanteme on a tested source program, an abstract syntax tree structure showing the structural information of the tested program control flow is output. An adjacency matrix of a control flow graph generates a module ergodicity abstract syntax tree structure, and generates the adjacency matrix representation of a program flow chart. A control flow path subset generating module acquires the control flow information of the tested program through traversing the adjacency matrix, traverses the adjacency matrix by adopting a depth-first multiple backtracking method, and processes sentence nodes, thus a program control flow path subset based on the base path is generated. The method has the outstanding advantages in generating results and flows, and can be widely used in the engineering practice of a path cover testing in a software structure testing.

The invention discloses an optimal allocation method of basin water resources based on a multi-objective chaotic genetic algorithm, and belongs to the technical field of optimal allocation of water resources. The method includes the steps of obtaining basic information of basin water resources; establishing a multi-objective water resource optimal allocation mathematical model and performing allocation model parameter calibration; solving a water resource optimal allocation alternative scheme set by using a multi-objective chaotic genetic algorithm; and finally, determining a best equilibrium scheme of water resource optimal allocation through a chaotic neutral network comprehensive evaluation model. According to the invention, the chaotic ergodicity and the inversion of the genetic algorithm are coupled, the speed of the algorithm is improved, the optimal solution is stable, and the multi-objective optimal allocation requirements of a basin water resource complex system are met.

The invention provides an image encryption method based on Duffing mapping and genetic operation. The hash value of a plaintext image is calculated by using a Keccak algorithm as an initial value input key of a chaotic system; the sensitivity and pseudo-randomness of the chaotic map to the initial conditions are used, the pseudo-random sequence is obtained by iterating the Logistic map and the Hill encryption matrix is generated to scramble and replace the image matrix. Combined with Duffing map and DNA coding technology, pixel selection, crossover and mutation are realized by genetic operation to achieve pixel diffusion and scrambling, and bidirectional exclusive OR operation with chaotic sequence to enhance its confusion and diffusion characteristics. The invention utilizes the pseudo-randomness of Duffing mapping and Logistic mapping, ergodicity and the crossover mutation operation of genetic algorithm, has strong sensitivity to key, can effectively resist statistical attack and differential attack, etc., has good security and application potential, and the image encryption effect and performance are remarkably improved.

The present invention discloses a hybrid energy storage capacity optimization configuration method in grid connected wind-solar generation. The energy management strategy is determined that storage battery work state optimization is taken as a principle and improving the system integration economical efficiency is taken as a target, based on the energy management strategy, the calculating flow of the energy loss rate and the energy deletion rate of the grid connected wind-solar generation system is analyzed, according to a total life-cycle cost theory, an annual average cost function expression of the energy storage device is built, and an energy storage capacity optimization configuration model taking the minimum function value as the target and taking the operation indexes such as the energy loss rate and the energy deletion as a constraint is built, and finally the optimization configuration model is solved by employing an improved chaotic optimization algorithm. The improved chaotic optimization algorithm employs the chaotic motion with ergodicity, randomness and regularity so as to effectively complete the calculation of the complex non-linear optimization configuration model.

The invention discloses a time-lag chaos iteration-based digital signature method and a time-lag chaos iteration-based digital signature device, and belongs to the technical field of communications. The process of generating a digital signature comprises the following steps: 1) performing ASCII encoding on a clear text and linearly quantizing a value domain; 2) performing one-dimensional time-lag chaos iteration; 3) performing a Hash functional transformation with a key; 4) encrypting, and the like. The process of authenticating the digital signature comprises the following steps: 5) performing the same step as the process of generating the digital signature on the received clear text and generating a Hash value; 6) decrypting the received digital signature (the Hash value); 7) authenticating, and the like. Due to the use of the sensitivity and the ergodicity of the time-lag chaos iteration, the clear-text information is modulated in the iteration track (process) of the time-lag chaos iteration, so that the generated Hash value has larger key space and higher safety. The time-lag chaos iteration-based digital signature method and the time-lag chaos iteration-based digital signature device are suitable for various secure transmission occasions of texts, video and audio files and the like which need the digital signature for verification, and have the characteristics of simple operation, high speed, irreversibility, anti-counterfeiting, strong attack and collision resistance, and the like.

The invention provides a complicated well hole track optimization method based on a fast self-adaption quantum genetic algorithm. The method comprises the following steps that through the analysis of an Fibonacci number sequence, the condition that the number sequence has a negative index characteristic is discovered, and the characteristic is introduced into a quantum rotation gate rotating angle step length updating strategy, wherein the space complexity of the algorithm is not increased, the time complexity of the algorithm is lowered, the algorithm efficiency is greatly improved, and the operation time of the algorithm is shortened; secondly, any one quantum position is enabled to be in one-to-one correspondence to points on a Bloch ball surface, so that the ergodicity of a solution is improved; finally, by aiming at a multi-target complicated three-dimensional well hole track optimization problem, under constraint conditions of each well section, casing pipe length and target vertical well depth, the FAQGA optimization is used for practically measuring the well depth TMD; a well body, a well inclined angle, a well inclination azimuth angle and well section curvature parameters are optimized, thus realizing the precise and efficient well hole track optimization.

The invention discloses an electronic nose parameter synchronous optimization algorithm based on an improved quantum particle swarm optimization algorithm. The method comprises performing wavelet transformation on obtained original electronic nose data; then performing weighting treatment of wavelet coefficients; through the improved quantum particle swarm optimization algorithm based on a novel local attractor computing manner, finding out a weighting coefficient corresponding to the highest electronic nose identifying rate, and classifier parameters to obtain a characteristic matrix of electronic nose signals; inputting the characteristic matrix into a classifier for mode identification. The electronic nose parameter synchronous optimization algorithm based on the improved quantum particle swarm optimization algorithm has the advantages of enhancing early-stage ergodicity and later-stage local optimizing capacity of particles, improving the capacity of quantum particle swarms in searching for global optimal values, and especially for wound infection detection, improving the identification rate of an electronic nose, thereby selecting appropriate treatment methods for doctors and providing beneficial guidance for promoting quick recovery of wounds.

The invention discloses a chaos-optimized orthogonal wavelet multi-mode blind equalization method (CO-WT-MMA). It includes the following steps: pass the transmitted signal a(k) through the impulse response channel h(k) to obtain the channel output vector x(k); use the channel noise n(k) and the channel output vector x(k) to obtain the orthogonal wavelet transformer ( WT) input signal y(k)=n(k)+x(k); After the real part and imaginary part of y(k) are subjected to orthogonal wavelet transform and chaos initialization respectively, and then through the corresponding real part and imaginary part The partial equalizer is output to the complex adder to get the output z(k). On the basis of the multi-mode blind equalization method (MMA), the multi-mode blind equalization method (WT-MMA) based on the orthogonal wavelet transform obtained after the normalized orthogonal wavelet transform accelerates the convergence speed, and at the same time utilizes The ergodicity of the chaotic variable disturbs the current point of the weight vector, and the time-varying parameters are used to gradually reduce the disturbance amplitude during the search process, so that the weight vector reaches the global optimal value. The underwater acoustic channel simulation results show that, compared with MMA and WT-MMA, the CO-WT-MMA of the present invention has faster convergence speed and smaller steady-state mean square error.

The invention discloses a road traffic flow prediction method. The method comprises the following steps: (1), collecting road traffic flow; (2), processing and cleaning data; (3), performing chaotic initialization on population particle parameters to be optimized; (4), adopting the optimized parameters for LSVVM sample training; (5), outputting prediction results after accuracy requirements are met. With the adoption of the road traffic flow prediction method based on a least square support vector machine optimized with an improved particle swarm algorithm, global optimal performance of particles is greatly improved by a chaotic ergodicity and variable weight combined model, so that parameters of a support vector machine model are relatively optimized, accuracy and speed of the predictionmodel are improved to a certain extent, the new data generalization prediction error of the model is lower, and generalization performance is improved.

The invention relates to a method for designing a fully randomized silicon-based waveguide optical grating on the basis of a chaotic particle swarm optimization algorithm. Uniform optical grating design parameters are made inhomogeneous, the overall variation of the optical grating is designed as the change of each periodic block, and a coupling efficiency value under the case of each parameter isan adaptation degree in the particle swarm optimization algorithm; when a particle swarm evolves to the next generation, each particle updates itself by tracking two optimal solutions including pbestand gbest; when a boundary position value is taken as a particle value, chaotic variable processing is conducted, and a global search function is achieved; an initial value is provided for a chaoticvariable, and a group of random sequences with ergodicity and pseudo-randomness are generated through iteration of a chaotic iterative equation; a coupling efficiency standard required for the opticalgrating is set, when a particle swarm optimization result reaches the required standard, the process stops automatically, and the particle value corresponding to the adaptation degree is the demandeddesign parameter value. Systematic design of the fully randomized optical grating is achieved, and design parameters and the coupling efficiency of the optical grating can be effectively and quicklyobtained.

A method for generating play-listing includes generating control signal for forming play-listing by utilizing audio play unit to respond user input, initializing multiple weighted value setting table, and sequencing various audio files according to sizes of weighted value to obtain weighted value and multiple field content of audio file in scope of preset number for calculating out weighted value of each sub item in said table then storing them separately in relevant weighted value setting table, carrying out ergodicity on said setting tables and calculating integration weighted value of each audio file for generating a play-listing according to weighted value of each audio file.

The invention provides a Sigma-Delta modulator self-adaptive mixing optimization method for improving the signal to noise ratio. The method includes creating a noise transfer function of a Sigma-Delta modulator and performing dimensionality reduction on noise transfer function parameters, optimizing the noise transfer function parameters subjected to dimensionality reduction by a differential evolution method based on self-adaptive Cauchy distribution and chaotic mapping, calculating to acquire optimal values of the noise transfer function parameters according to to-be-optimized optimal parameter values, determining the optimal noise transfer function to complete self-adaptive mixing optimization of the Sigma-Delta modulator, taking a sinusoidal signal output by an interpolation filter of a Sigma-Delta digital-to-analog converter as the input of the Sigma-Delta modulator with the optimized noise transfer function, transforming an output value of the Sigma-Delta modulator to a frequency domain, and calculating the signal to noise ratio of the Sigma-Delta modulator. According to the method, ergodicity of chaotic mapping and high disturbance of self-adaptive Cauchy distribution are fully utilized, a target function is created and is optimized by the mixing differential evolution method, and the signal to noise ratio is remarkably increased while the stability of the modulator is kept.

The invention provides an image encryption and decryption method based on four-dimensional quantum Dicke mapping and relates to the field of image encryption technology. The invention aims to solve the shortage of key space and insufficient randomness, as well as inability to effectively resist known plaintext attacks and security flaws in selective plaintext attacks in the existing image encryption methods. The invention uses a quantum Dicke chaotic system as a key generator to generate a plurality of sets of scrambling keys and diffusion keys, which are randomly selected by a user. The invention ensures the key space and the key sensitivity, shortens the length of the key and reduces the cost of storing and transmitting the key by the user. The invention applies the hyperchaotic system of the fractional-order quantum cellular neural network to the image diffusion stage, The hyperchaotic characteristic of the fractional-order quantum cellular neural network system guarantees the randomness and ergodicity of image diffusion, and the encryption method of the invention can effectively resist the known plaintext attack and the selected plaintext attack because the diffusion key streamis related to plaintext.

The invention discloses a power grid fault prediction method, and belongs to the technical field of power grid fault prediction. The power grid fault prediction method includes: determining a networkstructure of a BP neural network system according to input and output fault samples of the BP neural network system; using the fault samples as training samples of the BP neural network; and using thechaotic ant colony algorithm to train the BP neural network. The BP neural network and the chaotic ant colony algorithm are combined to predict a power grid fault; chaos initialization is performed by using the chaos ergodicity; and the positive feedback principle of the ant colony algorithm and chaotic disturbance are used to solve the problem of slow network convergence and capture local optimum in power grid fault prediction.

The invention relates to a chaotic particle swarm algorithm based on a discussion mechanism. The chaotic theory is used to initialize the population, so that the initial particles are uniformly distributed in the space, and the chaotic ergodicity is used for searching to make the algorithm jump out of the local optimum. The chaotic particle swarm algorithm is directed to the problem of low convergence accuracy of the existing algorithm, uses dual populations, and combines the discussion mechanism to enhance the convergence accuracy.

The invention discloses a phase diagram matrix method for nonlinear dynamic behavior analysis and belongs to the technical field of data recognition. The invention provides a novel phase diagram matrix method for nonlinear dynamic behavior analysis. The method can be used for analyzing nonlinear dynamic behaviors from the perspective of ergodicity and further identifying a chaos state of a system. According to the method, ergodicity parameters are creatively provided and can be used for quantitatively describing the ergodicity characteristics of chaos dynamic behaviors, and the calculation process is quick, simple and convenient, so that the identification speed of the chaos state can be greatly improved; according to the method, a phase diagram matrix is also provided, the size of the phase diagram matrix can be automatically set, and the ergodicity of a system phase space is investigated from different scales. The invention has an important application value in nonlinear system dynamics analysis, in particular to chaotic behavior recognition.

The invention discloses a particle cluster intelligent method based on a chaotic optimization mechanism. In the initialization phase, classical distribution and Logistic mapping are adopted; the particle swarm is initialized by Kent mapping; carrying out global optimization search in an N-dimensional solution space by utilizing a particle swarm algorithm with a weighted inertia weight factor. A function is updated by utilizing speed and position iteration. When the updating of the local optimal solution of the particles in the particle swarm falls into stagnation, global optimization search isperformed by utilizing the ergodicity of chaotic motion, the ergodicity interval of a chaotic variable is amplified to the definitional domain of an optimization variable in an appropriate carrier mode, particles are assisted to gradually escape from a local extreme value when the global optimal solution of the particle swarm is caught in stagnation, and the optimization search of the particle swarm is finished and the global optimal solution is output. According to the method, the defects that a premature phenomenon generally exists in a cluster intelligent algorithm and a globally optimal solution cannot be effectively obtained in complex high-dimensional function optimization are overcome.