Abstract: An information processing apparatus, includes a lower time series data generation unit having a plurality of recurrent neural networks which learn predetermined time series data, and generating prediction time series data. An upper time series data generation unit has recurrent neural networks which learn error time series data that is time series data of errors raised at the time of the learning by the respective plural recurrent neural networks of the lower time series data generation unit. Generation of prediction error time series data that is time series data of prediction errors; and a conversion unit that performs nonlinear conversion for the prediction errors generated by the upper time series data generation unit. The lower time series data generation unit outputs the prediction time series data according to the prediction errors.

Abstract: An information processing apparatus including a learning unit that learns a predetermined time-series pattern. An output unit outputs a time-series pattern corresponding to the result of learning by the learning unit. An adjusting unit supplied with a time-series pattern obtained from an action by an action unit on the basis of a time-series pattern supplied from the output unit and external teaching for the action adjusts a time-series pattern supplied from the output unit correspondingly to the input time-series pattern. The learning unit learns the time-series pattern supplied from the output unit and adjusted by the adjusting unit.

Abstract: An information processing method and an information processing apparatus in which the learning efficiency may be improved and the scale may be extended readily. An integrated module 42 is formed by a movement pattern learning module by a local expression scheme. The local modules 43-1 to 43-3 of the integrated module 42 are each formed by a recurrent neural network as a movement pattern learning model by a distributed expression scheme. The local modules 43-1 to 43-3 are caused to learn plural movement patterns. Outputs from the local modules 43-1 to 43-3, supplied with preset parameters, as inputs, are multiplied by gates 44-1 to 44-3 with coefficients W1 to W3, respectively, and the resulting products are summed together and output.

Abstract: This invention relates to an information processing device and method that enable generation of an unlearned new pattern. Data xt corresponding to a predetermined time series pattern is inputted to an input layer (11) of a recurrent neural network (1), and a prediction value x*t+1 is acquired from an output layer 13. A difference between teacher data xt+1 and the prediction value x*t+1 is learned by a back propagation method, and a weighting coefficient of an intermediate layer 12 is set at a predetermined value. After the recurrent neural network is caused to learn plural time series patterns, a parameter having a different value from the value in learning is inputted to parametric bias nodes (11-2), and an unlearned time series pattern corresponding to the parameter is generated from the output layer (13). This invention can be applied to a robot.

Abstract: An information-processing apparatus has a recurrent neural network containing an input node that allows data to be input, an output node that outputs data based on the data input through the input node, context input and output nodes, a context loop that returns a value indicating internal state in the network from the context output node to the context input node, and a recurrent loop that returns output from the network at predetermined time to the network as a next input to the network. The apparatus has a production device that produces a current input to the network by adding output from the output node into an immediately preceding input to the network at a predetermined rate and produces a current input to the context input node by adding output from the context output node into an immediately preceding input to the context input node at a predetermined rate.

Abstract: An information processing apparatus, comprises: a lower time series data generation unit having a plurality of recurrent neural networks which learn predetermined time series data, and generate prediction time series data according to the learning result; an upper time series data generation unit having recurrent neural networks which learn error time series data that is time series data of errors raised at the time of the learning by the respective plural recurrent neural networks of the lower time series data generation unit, and generate prediction error time series data that is time series data of prediction errors according to the learning result; and a conversion unit that performs nonlinear conversion for the prediction errors generated by the upper time series data generation unit, wherein the lower time series data generation unit outputs the prediction time series data generated by the respective plural recurrent neural networks according to the prediction errors which have undergone the nonlinear c

Abstract: Teaching to a robot for an action on an external object can be made easily and efficiently. According to the present invention, there is provided an information processing apparatus including a learning unit for learning a predetermined time-series pattern, an output unit for outputting a time-series pattern corresponding to the result of learning by the learning unit, and an adjusting unit supplied with a time-series pattern obtained from an action made by an action unit on the basis of a time-series pattern supplied from the output unit and external teaching for the action to adjust a time-series pattern supplied from the output unit correspondingly to the input time-series pattern, the learning unit learning the time-series pattern supplied from the output unit and adjusted by the adjusting unit.

Abstract: An information processing apparatus includes a first recurrent neural network (RNN) for performing processing which corresponds to a time-series and a second RNN for processing another correlated time-series. The difference between a context set output by the first RNN and a context set output by the second RNN is computed by a subtractor, and the obtained difference is used as a prediction error. Backpropagation is performed based on the prediction error, thus determining a coefficient for each neuron of an output layer, an intermediate layer, and an input layer.

Abstract: An information processing apparatus includes a first recurrent neural network (RNN) for performing processing which corresponds to a time-series and a second RNN for processing another correlated time-series. The difference between a context set output by the first RNN and a context set output by the second RNN is computed by a subtractor, and the obtained difference is used as a prediction error. Backpropagation is performed based on the prediction error, thus determining a coefficient for each neuron of an output layer, an intermediate layer, and an input layer.

Abstract: An information processing apparatus includes a first recurrent neural network (RNN) for performing processing which corresponds to a time-series and a second RNN for processing another correlated time-series. The difference between a context set output by the first RNN and a context set output by the second RNN is computed by a subtractor, and the obtained difference is used as a prediction error. Backpropagation is performed based on the prediction error, thus determining a coefficient for each neuron of an output layer, an intermediate layer, and an input layer.

Abstract: An information processing apparatus includes a first recurrent neural network (RNN) for performing processing which corresponds to a time-series and a second RNN for processing another correlated time-series. The difference between a context set output by the first RNN and a context set output by the second RNN is computed by a subtractor, and the obtained difference is used as a prediction error. Backpropagation is performed based on the prediction error, thus determining a coefficient for each neuron of an output layer, an intermediate layer, and an input layer.

Abstract: An information processing apparatus includes a first recurrent neural network (RNN) for performing processing which corresponds to a time-series and a second RNN for processing another correlated time-series. The difference between a context set output by the first RNN and a context set output by the second RNN is computed by a subtractor, and the obtained difference is used as a prediction error. Backpropagation is performed based on the prediction error, thus determining a coefficient for each neuron of an output layer, an intermediate layer, and an input layer.

Abstract: An information processing apparatus includes a first recurrent neural network (RNN) for performing processing which corresponds to a time-series and a second RNN for processing another correlated time-series. The difference between a context set output by the first RNN and a context set output by the second RNN is computed by a subtractor, and the obtained difference is used as a prediction error. Backpropagation is performed based on the prediction error, thus determining a coefficient for each neuron of an output layer, an intermediate layer, and an input layer.

Abstract: An information processing apparatus includes a first recurrent neural network (RNN) for performing processing which corresponds to a time-series and a second RNN for processing another correlated time-series. The difference between a context set output by the first RNN and a context set output by the second RNN is computed by a subtractor, and the obtained difference is used as a prediction error. Backpropagation is performed based on the prediction error, thus determining a coefficient for each neuron of an output layer, an intermediate layer, and an input layer.

Abstract: This invention relates to an information processing device and method that enable classification of a new time series pattern. A time series pattern N of a curve L (21) is inputted to an output layer (13) of a recurrent neural network 1. An intermediate layer (12) has already learned a predetermined time series pattern, and a weighting coefficient corresponding to that time series pattern is held in its neurons. The intermediate layer (12) calculates a parameter corresponding to the time series pattern N on the basis of the weighting coefficient and outputs the calculated parameter from parametric bias nodes (11-2). A comparator unit (31) compares a parameter of a learned pattern stored in a storage unit (32) with the parameter of the time series pattern N and thus classifies the time series pattern N. This invention can be applied to a robot.

Abstract: An information processing method and an information processing apparatus in which the learning efficiency may be improved and the scale may be extended readily. An integrated module 42 is formed by a movement pattern learning module by a local expression scheme. The local modules 43-1 to 43-3 of the integrated module 42 are each formed by a recurrent neural network as a movement pattern learning model by a distributed expression scheme. The local modules 43-1 to 43-3 are caused to learn plural movement patterns. Outputs from the local modules 43-1 to 43-3, supplied with preset parameters, as inputs, are multiplied by gates 44-1 to 44-3 with coefficients W1 to W3, respectively, and the resulting products are summed together and output.

Abstract: This invention relates to an information processing device and method that enable generation of an unlearned new pattern. Data xt corresponding to a predetermined time series pattern is inputted to an input layer (11) of a recurrent neural network (1), and a prediction value x*t+1 is acquired from an output layer 13. A difference between teacher data xt+1 and the prediction value x*t+1 is learned by a back propagation method, and a weighting coefficient of an intermediate layer 12 is set at a predetermined value. After the recurrent neural network is caused to learn plural time series patterns, a parameter having a different value from the value in learning is inputted to parametric bias nodes (11-2), and an unlearned time series pattern corresponding to the parameter is generated from the output layer (13). This invention can be applied to a robot.

Abstract: This invention provides a data processing apparatus which can store and recall more complicated time-series data than those processed in related art technologies. In the data processing apparatus, a recurrent neural network (RNN) of higher layer generates long-period parameter and supplies it to an input layer of RNN of lower layer via a computing block. The RNN uses this input as a parameter and computes short-period input.

Abstract: A learning-type movement control apparatus that learns the movement of an operation control device, predicts the movement thereof, and drives it so as to automatically move. The apparatus comprises an operation control device having a predetermined portion that is displaced according to a force exerted in an arbitrary direction, outputs the amount of the displacement at least as one-dimensional position-representing information, receives a feedback signal carrying information generated by adding displacement information to the position-representing information, and drives the predetermined portion according to a direction and a displacement that are based on the feedback signal. The apparatus also includes a learning section that receives the position-representing information and performs learning of the movement of the operation control device.

Abstract: This invention provides a data processing apparatus which can store and recall more complicated time-series data than those processed in related art technologies. In the data processing apparatus, a recurrent neural network (RNN) of higher layer generates long-period parameter and supplies it to an input layer of RNN of lower layer via a computing block. The RNN uses this input as a parameter and computes short-period input.