Eureka-AI is an intelligent assistant for R&D personnel, combined with Patent DNA, to facilitate innovative research.
Eureka AI

2227 results about "Genetic algorithm" patented technology

In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on bio-inspired operators such as mutation, crossover and selection. John Holland introduced genetic algorithms in 1960 based on the concept of Darwin’s theory of evolution; afterwards, his student David E. Goldberg extended GA in 1989.

Method for Capturing the Essence of Product and Service Offers of Service Providers

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.

Task scheduling method based on heredity and ant colony in cloud computing environment

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.

Methods and systems for identification of DNA patterns through spectral analysis

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.

Method for controlling PMSM (permanent magnet synchronous motor) servo system based on friction and disturbance compensation

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.

Performance of artificial neural network models in the presence of instrumental noise and measurement errors

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).
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products