Toyoda Koki Kabushiki Kaisha

Other

Kabushiki Kaisha Toyoda Jidoshokki Seisakusho

Tomonari Kato

Takao Yoneda

Toshihiro Yonezu

Kazuya Hattori

Kazuya Kimura

Masahiko Okada

Akifumi Uryu

Makoto Nonoyama

Masashi Yamanaka

Yutaka Hayashi

Yukinori Kakazu

Shiho Hattori

Nobuo Ohkubo

A power transmission mechanism for power-transmittably coupling a rotating unit of a compressor (,) composed of a drive shaft (,), a rotating support (,) and a swash plate (,) with an engine (,), comprising a pulley (,) and an armature (,), both of which constitute a first rotating body provided on a side of the engine, a hub (,), which serve as a second rotating body coupled to the drive shaft of the compressor, and a spring (,), which serves as an elastic means for coupling the first and second rotating bodies. A spring constant of the spring (,) is set such that a resonant frequency (fR) determined by the spring constant and a sum of a moment of inertia of the rotating unit of the compressor and a moment of inertia of the second rotating body as dominant factors is made smaller than a minimum frequency (f1) of torque variation produced on the compressor and more preferably smaller than a minimum frequency (f2) of torque variation produced on the engine.

Power transmission mechanism

A machining apparatus for machining a workpiece by moving a tool in direction relative to the workpiece comprises, tool moving means for moving the tool in the direction relative to the workpiece, tool movable range setting means for setting a movable range for the tool to inhibit the workplace from exceeding a reference size by inputting the reference size, and halting means for halting a movement of the tool when a position of said tool exceeds the tool movable range set by said tool movable range setting means.

Method for machining a workpiece by renewing a tool movable range

An apparatus for carrying out learning operation of a neural network which has an input layer, an intermediate layer and an output layer. Plural nodes of the input layer are related to plural nodes of the intermediate layer with plural connection weights, while the plural nodes of the intermediate layer are also related to plural nodes of the output layer with plural connection weights. Although input data composed of plural components and teaching data composed of plural components are used in learning operation, some of the components are ineffective for the purpose of learning operation. During error calculation between output data from the neural network and the teaching data, the apparatus judges whether each of the components of the teaching data is effective or ineffective, and output errors corresponding to the ineffective components are regarded as zero. The connection weights are thereafter corrected based upon the calculated errors. The apparatus further comprises means for adding new input data and teaching data into a data base, and means for calculating a degree of heterogeneousness of the new teaching data. The new input data and teaching data are added to the data base only when the degree of heterogeneousness is smaller than a predetermined value.

Method and apparatus for performing learning in a neural network

An apparatus for checking the input-output characteristic of a neural network which has an input layer, an intermediate layer and an output layer. Plural nodes of the input layer are related to plural nodes of the intermediate layer with plural connection weights while the plural nodes of the intermediate layer are also related to plural nodes of the output layer with plural connection weights. One of the nodes of the input layer is selected as a variable input element while the rest of the nodes are regarded as fixed input elements. A variable data input to the variable input element is varied within a predetermined variation range, while data input to the fixed input elements are fixed, so as to detect how the components of output data from the output layer change. The detected changes of the components of the output data are displayed as the input-output characteristic of the neural network.

Method and apparatus for checking input-output characteristic of neural

A digital servo-control apparatus which controls the rotation of a servomotor on the basis of a digital position command value representative of a target position and a digital position feedback value output from a position detecting device. The apparatus comprises a device for calculating a feedforward value at intervals of a predetermined period on the basis of the amount of change of the position command value, a device for delaying the position command value by a period of time corresponding to an operation delay in the feedforward value calculating device, a device for calculating a velocity command value by adding the feedforward value to a deviation of the position feedback value from the position command value delayed by the delay device, and a device for driving the servomotor according to the velocity command value determined by the calculating device.

Digital servo-control apparatus

Growing community of inventors

Kariya, Japan

Tomonari Kato