2227 results about "Genetic algorithm" patented technology

The present invention provides systems and methods for dynamic detection and prevention of electronic fraud and network intrusion using an integrated set of intelligent technologies. The intelligent technologies include neural networks, multi-agents, data mining, case-based reasoning, rule-based reasoning, fuzzy logic, constraint programming, and genetic algorithms. The systems and methods of the present invention involve a fraud detection and prevention model that successfully detects and prevents electronic fraud and network intrusion in real-time. The model is not sensitive to known or unknown different types of fraud or network intrusion attacks, and can be used to detect and prevent fraud and network intrusion across multiple networks and industries.

To provide a region detecting method capable of setting a proper threshold independently of a photographing condition and moreover, quickly and accurately detecting a specific region, which uses an image data storing section 10 for storing a sample image 12 and a purposed image 11, a first computing means 1 for obtaining the extraction-region-identifying threshold data for a plurality of sample images including a common extraction region but having average color values different from each other in accordance with a genetic algorithm and generating a threshold table 8 for the average color values, a second computing means 2 for adaptively computing the extraction-region-identifying threshold data for the purposed image 11 in accordance with the average color value of the purposed image and the threshold table 8, and a third computing means 3 for detecting an extraction region in accordance with the threshold data computed by the second computing means 12.

Disclosed are a computer-readable code, system and method for generating candidate novel concepts in one or more selected fields. The system operates to generate strings of terms composed of combinations of word and optionally, word-group terms that are descriptive of concept elements in such field(s), and uses a genetic algorithm to find one or more high fitness strings, based on the application of a fitness metric which quantifies, e.g., the number occurrence of pairs of terms in texts in a selected library of texts. The highest- score string or strings are then applied in a database search to identify one or more pairs of primary and secondary texts whose terms overlap with those of a high fitness string.

Online fraud prevention including receiving a rules set to detect fraud, mapping the rules set to a data set, mapping success data to members of the rules set, filtering the members of the rules set, and ordering members of the data set by giving priority to those members of the data set with a greater probability for being fraudulent based upon the success data of each member of the rule set in detecting fraud. Further, a receiver coupled to an application server to receive a rules set to detect fraud, and a server coupled to the application server, to map the rules set to a data set, and to map the success data to each members of the rules set. The server is used to order the various members of the data set by giving priority to those members of the data set with a greatest probability for being fraudulent.

The invention relates to a flight path planning method based on a sparse A* algorithm and a genetic algorithm and belongs to the technical field of flight path planning of an unmanned aerial vehicle (UAV). According to the characteristics of flight path planning, the method comprises the following steps: planning an initial reference flight path by the utilization of the sparse A*search (SAS) algorithm, wherein constraint conditions are combined into algorithm search, so that useless nodes in a search space can be effectively deleted and the search time is shortened; and when emergent threat occurs during real-time flight of the UAV, performing real-time flight path planning by the utilization of the genetic algorithm and generating a flight path with local optimum or approximate optimum until the threat disappears and the UAV returns the original global optimum reference flight path and continues flying. The method provided by the invention has high real-time performance and rapidity; the searched flight path is closer to the actual UAV optimal flight path; and the method can be applied to the technical fields of robot path planning, urban vehicle path planning and the like under complex environments.

The invention discloses a vision-inertia integrated SLAM (Simultaneous Localization and Mapping) method based on a genetic algorithm. The method comprises the following steps: integrating a vision navigation coordinate system with an inertia navigation coordinate system, and calibrating parameters of a binocular camera so as to solve a three-dimensional space coordinate according to an image pixel coordinate; independently calculating by inertia navigation; calculating by vision navigation; integrating vision navigation information with inertia navigation information by using an extended Kalman filter, and building a system filter model; and observing global feature point road signs by using the binocular camera by taking localization locality into account, carrying out data association on map features based on the genetic algorithm, and feeding extended state vectors back to a filter. The method is capable of carrying out long-time and high-accuracy localization; the genetic algorithm is added for improving the data association of the map, so that the simultaneous mapping accuracy is greatly improved.

A present novel and non-trivial system, module, and method for constructing a flight path used by an avionics system are disclosed. A processor receives flight plan data and object data associated with terrain and obstacles. Free cells are extracted above the objects using a recursive space decomposition technique, and a reference path is formed through traversable free space determined from the availability of free cells. In an additional embodiment, threat data associated with hostile military weaponry and significant meteorological conditions could affect the availability of free cells. A genetic algorithm applying genetic operators which include mutators is employed with aircraft kinematic constraints to refine the reference path used to form a population of best path candidates. When a best path is reached after cycling through a re-generation process of path candidates, flight path data representative of the best path is generated and provided to at least one avionics system.

A system generates optimal global set points for an environmental management system. The system comprises a system model for modeling components of a thermal plant, an objective function for modeling a parameter of the thermal plant, and an optimization engine for optimizing the parameter modeled by the objective function. The system model is coupled to an input data collector for receiving building data and weather data corresponding to a particular site. The system model includes models for thermal plant system components that may be implemented using classical models or artificial intelligence models. Classical models are those models that are implemented using linear programming, unconstrained non-linear programming, or constrained non-linear programming methodologies. The artificial intelligence models are those models that may be implemented using a fuzzy expert control system with crisp and fuzzy rules, genetic algorithms for optimization, or neural networks.

The invention discloses a three-dimensional formation reconfiguration method for multiple unmanned aerial vehicles based on particle swarm optimization and genetic algorithm. The method considers the position of the unmanned aerial vehicle in the ground coordinates and the speed, track angle and course angle of the unmanned aerial vehicle when establishing a formation model, carries out subsection linear disposal of the control input of each flying unit in the unmanned aerial vehicle, replaces the approximate subsection linear control input with the continuous control input, then carries out global search by the genetic algorithm, subsequently carries out partial searching by the particle swarm optimization algorithm, on the base thereof, the particle swarm optimization is used to guide the genetic algorithm to search a global optimum solution so as to figure out the subsection linear control input. Compared with the traditional method, the method provided by the invention has good real-time performance and rapidity and can be used for solving the formation reconfiguration problem of multiple space robots under complex and dynamic environment.

Systems and methods are provided for receiving order information based on an order of a customer; and determining an offer for the customer based on the order information and at least one of a genetic program and a genetic algorithm.

A wavelet-based band difference-sum ratio method reduces the computation cost of classification and feature extraction (identification) tasks. A Generalized Difference Feature Index (GDFI), computed using wavelets such as Daubechies wavelets, is employed in a method to automatically generate a large sequence of generalized band ratio images. In select embodiments of the present invention, judicious data mining of the large set of GDFI bands produces a small subset of GDFI bands suitable to identify specific Terrain Category/Classification (TERCAT) features. Other wavelets, such as Vaidyanathan, Coiflet, Beylkin, and Symmlet and the like may be employed in select embodiments. The classification and feature extraction (identification) performance of the band ratio method of the present invention is comparable to that obtained with the same or similar data sets using much more sophisticated methods such as discriminants, neural net classification, endmember Gibbs-based partitioning, and genetic algorithms.

A method includes: a) producing initial scheduling plans based on requests related to tasks to be performed within a time period by a remote sensing satellite; wherein each initial scheduling plan schedules respective tasks, which do not conflict with each other in time and in using satellite resources; and each task is scheduled in at least one of the initial scheduling plans; b) applying a genetic-algorithm-based processing to the initial scheduling plans to produce a genetic-algorithm-based scheduling plan which is for mission objectives, and complies with constraints related to the satellite resources, to the tasks, and to the time period; and c) applying a simulated-annealing-based processing to the genetic-algorithm-based scheduling plan to produce a simulated-annealing-based scheduling plan that fits the mission objectives, complies with the constraints, and in which a larger number of tasks is scheduled than in the genetic-algorithm-based scheduling plan.

A system for generation of business executables using artificial intelligence or rules distributed among multiple hardware devices, including: a memory element of a specially programmed general-purpose computer storing a first rule or an artificial intelligence (AI) program; a generating element, in a processor for the specially programmed computer, arranged to generate at an executable using at least one of the first rule or the AI program; an interface element of the specially programmed computer, arranged to receive a second rule from a first wireless communications device (WCD), or from a general-purpose computer associated with a business entity and to store the second rule in the memory element; and a modifying element, in the processor, arranged to modify the executable using the second rule and to transmit, using the interface element, the modified executable to a wireless communications network for transmission to a second WCD.

A method and system managing radio frequency identification (RFID) systems. The system monitors the performance of systems that use RFID tags and readers. It operates at multiple levels to ensure the optimal performance of readers, tags, antennae, and the information processing systems that acquire and convey tag data. The management system may employ artificial intelligence techniques such as genetic algorithms, fuzzy logic, neural networks, Bayesian networks, support vector machines or statistical methods to develop, maintain and exploit models of RFID system behavior. By comparing the actual performance of RFIDs and related components, the management system can detect and report failures and partial failures of components. The management system may also send signals to components to enhance performance of the overall RFID system.

The present invention applies genetic algorithmic generation of test cases the simulation of VLSI logic circuit blocks. The present invention generates a number of original test cases. This aggregate of solutions is provided to a circuit simulator. The results of the simulator are maintained in a matrix or table. The results detail the number of times that particular logic states or events associated with the VLSI block have been stimulated by particular test cases. The aggregate of solutions and the simulation results are then analyzed by the genetic algorithm. The genetic algorithm preferably identifies states associated with the circuit simulation that have not been produced by the original test cases. The genetic algorithm then combines characteristics of various test cases to generate new test cases. The new test cases are provided to the circuit simulator thereby providing a higher degree of confidence that the entire VLSI chip design has been simulated.

The present invention reduces overhead in a VLIW type microprocessor including a dynamic compiler or controls a memory capacity for storing an object code after scheduling. The present invention relates to a VLIW microprocessor including a dynamic compiler and improves operation performance of a microprocessor by executing instructions more efficiently. Specifically, one feature of the present invention is to reduce overhead accompanying execution of a dynamic compiler and to control a memory capacity for storing an object code after scheduling internal instructions by using genetic algorithm (GA) in an execution of instructions in a VLIW microprocessor including a dynamic compiler.

Apparatus and method for at least partially fitting a medical implant system to a recipient is described. The medical implant system is configured to provide electrical stimulation and at least one other mode of stimulation to the recipient. These apparatuses and methods comprise executing a genetic algorithm to select a set of parameter values for the medical implant system. This genetic algorithm may comprise generating successive generations of child populations and then providing a determined set of parameter values to the medical implant system for use in providing stimulation to the recipient.

A computer implemented method of constructing a computer implemented knowledge base, of evaluating a plurality of invoices, of knowledge refinement and generation, as well as a computer implemented knowledge base for analyzing a plurality of invoices. The methods comprise receiving the invoices, semantic and logically analyzing them to identify the invoice items (parameters and algorithms of service providers, billing plans, user profile, consumption pattern and debits) and relations connecting them and construct the knowledge base. The knowledge base comprises a hierarchic taxonomy of billing plans related to services of any domain (telecommunications services, banking, insurance, utilities etc.) and a computer implemented generic invoice constructed in reverse engineering logic for simulating debits. Debit simulations are done in order to achieve: 1. recommendations for optimal billing plans. 2. Recommendations for possible detected billing errors. 3. recommendations concerning new plans and/or services, and their financial implications Improving the knowledge base may use genetic algorithms based on an analogous hierarchic structure of the taxonomy to a genetic hierarchy, and may proceed by refining billing plans and comparing the resulting debits. Novel Semantic-web and Artificial Intelligence (AI) methods are used.

Provided in the invention is a task scheduling method based on heredity and ant colony in a cloud computing environment. The method comprises the following methods: S1, initializing population; S2, selecting individuals according to a wheel disc type selection strategy; S3, carrying out crossover operation on the individuals according to crossover probability and carrying out reversion mutation operation according to a mutation probability so as to generate a new colony; S4, updating the new generated colony; S5, determining whether a dynamic fusion condition is met; S6, initializing ant pheromone by using an optimal solution found by heredity; S7, calculating probabilities of moving to next nodes by all ants and moving all the ants to the next nodes according to the probabilities; S8, enabling M ants to travelling N resource nodes and carrying out pheromone updating on an optimal ant cycle; S9, carrying out pheromone updating on all paths; and S10, determining whether an ant end condition is met and outputting an optimal solution. According to the invention, respective advantages of a genetic algorithm and an ant colony algorithm are drawn and respective defects are overcome; and on the basis of dynamic fusion of the two algorithms, time and efficiency of exact solution solving are both considered.

There is provide a mobile radio apparatus capable of suppressing a mismatching loss, and increasing a transmission/reception sensitivity by means of instantly and adaptively matching the impedance between the antenna and the transmission/reception circuit whatever situation a mobile radio apparatus, such as a mobile phone or the like, may be. In a mobile radio apparatus (1), when starting control of the matching circuit (102), a control section (105) evaluates an initial chromosome stored in a storage section (106), and if there is an initial chromosome for providing an impedance matching, controls the matching circuit (102) so as to have a load value corresponding to this initial chromosome. If there is no initial chromosome for providing the impedance matching, the control section (105) evolves the initial chromosome with a genetic algorithm, derives a chromosome for providing the impedance matching, and controls the matching circuit (102) so as to have a load value corresponding to the derived chromosome.

The invention provides a cloud task scheduling method based on an improved genetic algorithm which is the dual fitness multiplied genetic algorithm, and relates to the fields of cloud computing and scheduling algorithms. As for a cloud computing Map/Reduce programming model, the problem of matching between user tasks and virtual resources is researched to find appropriate resources for execution of the user tasks, the total task execution time and the mean task execution time are shortest at the same time through the algorithm, and according to the algorithm, one fitness and one multiplied algorithm comprehensively adopting task scheduling are added. An initial population is generated, the individual fitness value is calculated, the operations of selection, intersection and mutation are carried out, the number of iteration is added by one, new populations are generated constantly, the individual fitness values of the finally-obtained population are calculated respectively, the individual with the largest fitness value is the optimal solution, and a resource node sequence obtained by decoding the individual is the final result of the cloud task scheduling method based on the improved genetic algorithm.

A greenhouse environment forecasting feedback method of a back propagation (BP) neural network based on the improvement of a genetic algorithm comprises the following steps: obtaining a plurality of groups of temperatures of plant leaves to be tested and monitoring data values of other five factors which have influence on the temperatures, normalizing the data into values from -1 to +1, and classifying the normalized data into groups to serve as training and testing data for use; establishing the BP neural network which comprises an input layer, a middle layer and an output layer; using the genetic algorithm to optimize the established BP neural network; training and testing the optimized BP neural network; and forecasting the temperatures of the plant leaves according to the BP neural network which passes the test. Through the greenhouse environment forecasting feedback method of the BP neural network based on the improvement of the genetic algorithm, a model suitable for greenhouse environment parameter forecasting can be established, and target parameters in a greenhouse environment can be forecasted accurately and rapidly through a computer simulation test.

Spectrogram extraction from DNA sequence has been known since 2001. A DNA spectrogram is generated by applying Fourier transform to convert a symbolic DNA sequence consisting of letters A, T, C, G into a visual representation that highlights periodicities of co-occurrence of DNA patterns. Given a DNA sequence or whole genomes, with this method it is easy to generate a large number of spectrogram images. However, the difficult part is to elucidate where are the repetitive patterns and to associate a biological and clinical meaning to them. The present disclosure provides systems and methods that facilitate the location and/or identification of repetitive DNA patterns, such as CpG islands, Alu repeats, tandem repeats and various types of satellite repeats. These repetitive elements can be found within a chromosome, within a genome or across genomes of various species. The disclosed systems and methods apply image processing operators to find prominent features in the vertical and horizontal direction of the DNA spectrograms. Systems and methods for fast, full scale analysis of the derived images using supervised machine learning methods are also disclosed. The disclosed systems and methods for detecting and/or classifying repetitive DNA patterns include: (a) comparative histogram method, (b) feature selection and classification using support vector machines and genetic algorithms, and (c) generation of spectrovideo from a plurality of spectral images.

The invention discloses a method for controlling a PMSM (permanent magnet synchronous motor) servo system based on friction and disturbance compensation. In the method, a feedforward compensation method based on a friction model is combined with an auto disturbance rejection technology and the feedforward compensation method is complementary with the auto disturbance rejection technology mutually. In the method, a Stribeck friction model is utilized to carry out modeling on system frictions, a GA (genetic algorithm) is adopted to carry out offline identification on parameters, and an estimated value generated by an identification model carries out feedforward compensation; a state observer in the auto disturbance rejection technology observes and compensates overcompensation or undercompensation of the frictions as well as nondeterminacy and external disturbance caused by modeling errors in the system; and finally a differential tracker and a nonlinear control law are used to arrange a transient process for fixed position signals, thus solving the conflict between rapidity and overstrike and ensuring stability of the system and finite time convergence. By using the combined control, the compensation capacity of the system for the frictional nonlinearity can be improved effectively, the low-speed performance of the system is improved, and the tracking accuracy and the anti-disturbance capacity of the system are enhanced.

A private network system operates over a public network to provide an asymmetric service to members with information from other members and non-members. The system includes central servers and databases connected via the public network to client systems of public network users. The users may be members who communicate with a private protocol or non-members who communicate with the public protocol. A process for the private service includes communication of information between users via the private service servers. The private service server implements the private service, which includes a viral engine for network deployment. Features of the viral engine include genetic algorithms, data mining, personalization, frictionless service setup, user maximization, and member-controlled privacy. A sample setup process and contact update wizard that include several viral engine features are described.

A subset of quality metrics as input to a disk drive failure prediction algorithm (DFPA) may be selected using a genetic algorithm. The DFPA is executed for an initially selected generation of subset quality metrics using quality metric values stored in a reference data base. At least one DFPA setting is adjusted and the DFPA executed again for the selected subset. After training the DFPA, the best DFPA setting is saved for the selected subset. A fitness score is generated for the selected subset, representing an accuracy of the DFPA relative to failure indicators stored in the reference data base. At least one genetic operator is applied in response to the fitness scores to generate a new generation of subsets. The process is repeated until a best subset of quality metrics and corresponding DFPA setting are found.

A method is described for improving the prediction accuracy and generalization performance of artificial neural network models in presence of input-output example data containing instrumental noise and/or measurement errors, the presence of noise and/or errors in the input-output example data used for training the network models create difficulties in learning accurately the nonlinear relationships existing between the inputs and the outputs, to effectively learn the noisy relationships, the methodology envisages creation of a large-sized noise-superimposed sample input-output dataset using computer simulations, here, a specific amount of Gaussian noise is added to each input/output variable in the example set and the enlarged sample data set created thereby is used as the training set for constructing the artificial neural network model, the amount of noise to be added is specific to an input/output variable and its optimal value is determined using a stochastic search and optimization technique, namely, genetic algorithms, the network trained on the noise-superimposed enlarged training set shows significant improvements in its prediction accuracy and generalization performance, the invented methodology is illustrated by its successful application to the example data comprising instrumental errors and/or measurement noise from an industrial polymerization reactor and a continuous stirred tank reactor (CSTR).

A method and device for performing a quantum algorithm where the superposition, entanglement with interference operators determined for performing selection, crossover, and mutation operations based upon a genetic algorithm. Moreover, entanglement vectors generated by the entanglement operator of the quantum algorithm may be processed by a wise controller implementing a genetic algorithm before being input to the interference operator. This algorithm may be implemented with a hardware quantum gate or with a software computer program running on a computer. Further, the algorithm can be used in a method for controlling a process and a relative control device of a process which is more robust, requires very little initial information about dynamic behavior of control objects in the design process of an intelligent control system, or random noise insensitive (invariant) in a measurement system and in a control feedback loop.

A system and method are provided for the training and testing of prediction algorithms. According to an exemplary embodiment of the invention the method generates optimum training, testing and/or validation data sets from a common general database by applying a genetic algorithm to populations of testing and training subsets used in connection with a given prediction algorithm. In exemplary embodiments the prediction algorithm operated upon is an artificial neural network. As well, in preferred exemplary embodiments, the most predictive independent variables of the records of the common database are automatically selected in a pre-processing phase. Such preprocessing phase applies a genetic algorithm to populations of prediction algorithms which vary as to number and content of input variables, where the prediction algorithms representing the selections of input variables which have the best testing performances and the minimum input variables are promoted for the processing of the new generations according to a defined selection algorithm.

The invention relates to a WSN routing optimization method based on improved PSO. According to the method, a relational matrix containing topological structure information of the whole network serves as a PSO coding manner and is used for handling the problem of routing optimization; and intersection and variation mechanisms of a genetic algorithm are used to realize global convergence and search. The method comprises the steps of initializing parameters, initializing swarms, calculating the particle fitness, searching for Pbest and Gbest, introducing the intersection and variation mechanisms, and updating the Pbest and Gbest. According to the invention, the structure is simple, the intersection and variation mechanisms of the genetic algorithm are used to realize global convergence and search, optimized solution is realized, generation of a redundancy space and redundancy search are reduced via the relation matrix coding manner, and the instantaneity and stability of the method are improved.