36 results about "Learning automata" patented technology

The invention relates to a development automatic machine with a brain cognition mechanism and a learning method of the development automatic machine, and belongs to the technical field of intelligent robots. The development automatic machine comprises an inner state set, a system output set, an inner operation behavior set, a state transfer equation, a reward signal, a system evaluating function, a system action selecting probability and a Dopamine response differential signal. By means of the development automatic machine and the learning method, a mathematic model high in generalization ability and wide in application range is provided for the system autonomous development process with the learning automatic machine as the basic frame; by means of the method, a sensorimotor system and an intrinsic motivation mechanism are combined, the self-learning and self-adaption capacity of the system is improved, and the intelligence in the true sense is achieved.

The invention discloses a cognitive and learning method of a cognitive moving system with an inner engine. The cognitive model system to study the basis of automatic machines, including perceived state set, the set of actions, orientation map collection, curiosity, orientation function, learning orientation matrix, the state transition function and knowledge entropy, more than 10 parts. Perception of the current state of the system model; select action mechanism based on the engine; perform an action, state transition occurs; calculated value orientation function; Updates 'Perception - Movement'mapping; the process is repeated until the entropy minimal knowledge or learning time is greater than terminated time. The invention introduces the engine mechanism with active learning environment, not only the system has a strong self-learning and self-organizing ability, and can effectively avoid damaging small probability events, improve the stability of the system, for the establishment of having robot cognitive development capability to provide a strong foundation.

The invention discloses a false positive gene mutation filtering method for targeted capture of gene sequencing data, which preprocesses the detection data of gene mutation; selects three different supervised learning algorithms based on the triple training method to construct three different initial classifications Device H 1 , H 2 , H 3 , that is, select three different supervised learning automata and generate a learner based on the initial training set; for H 1 , H 2 , H 3 Perform training to obtain an expanded training set, thereby updating the model; use the trained model to mark the unlabeled sample set U, and complete the filtering according to the marking results. The invention solves the problem that the traditional method cannot effectively deal with batch differences.

The present invention provides a medical resource scoring and recommendation method based on a latent semantic model. The steps are as follows: the first step is to obtain the data of recommended resource items, and perform data filtering and cleaning; the second step is to use continuous action learning automata to train SVD++ Each parameter in the model; the third step, for the user, for all resource items in the set, calculate the predicted value of the user’s rating for each resource item based on the hidden semantic model; the fourth step, use the improved sorting algorithm to get TopN recommendation list. The present invention regards the predicted scoring results and the degree of correlation with the most relevant hidden class as the basis for generating the recommendation list, and also refers to the reference coefficient of the resource item itself, which can ensure that the recommended resource item is more likely to arouse the interest of the patient, and also It can be guaranteed that the resource item has a high enough score to be favored by the patient.

Due to the fact that the vehicle nodes rapidly move in the Internet of Vehicles and the topology of the Internet of Vehicles is highly dynamically changed, the Internet of Vehicles is prone to data aggregation, delay and the like, and great challenges are brought to the network communication and stability of the Internet of Vehicles to a great extent. However, a good Internet of Vehicles routing strategy not only needs to keep the rapid connection of the network, but also needs to keep the stability of the network, namely, the accessibility of the network is ensured. Therefore, the analysis and understanding of the accessibility in the Internet of Vehicles community are an urgent problem to be solved. The invention aims to solve the problems, In order to detect the communication inside theInternet of Vehicles community and keep stable, the accessibility method in the Internet of Vehicles community is provided; According to the method, a learning automaton theory is utilized, corresponding excitation functions and penalty functions are set through information exchange and competition deployed among community nodes, forwarding probabilities of different routes are adjusted in a self-adaptive mode, the Nash equilibrium state is achieved, and therefore the purposes of optimizing data transmission in the network on the whole and improving the accessibility of the Internet of Vehicles network are achieved.

The invention discloses a method for forming a multi-agent distributed union. According to the method disclosed by the invention, when agents accept different tasks, the appropriateness of the capabilities of the agents and the tasks is different; a small union having two agents is used as an ideal basis unit; and, in combination with task earning characteristics, dynamic distribution of weight values is carried out through a Learning Automata algorithm. The condition that expression is carried out in two aspects including the task angle and the agent angle is sufficiently considered; the method accords with the current situation well; the method is more rational; furthermore, concurrent choice of the tasks is realized; and the task distribution speed and effective utilization of agent resources are enhanced.