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    • 1. 发明授权
    • Neural network circuit
    • 神经网络电路
    • US5636327A
    • 1997-06-03
    • US409949
    • 1995-03-23
    • Hiroyuki NakahiraShiro SakiyamaMasakatsu MaruyamaSusumu Maruno
    • Hiroyuki NakahiraShiro SakiyamaMasakatsu MaruyamaSusumu Maruno
    • G06N3/063G06F15/18
    • G06K9/4628G06K9/00986G06N3/063
    • In a multilayered neural network for recognizing and processing characteristic data of images and the like by carrying out network arithmetical operations, characteristic data memories store the characteristic data of the layers. Coefficient memories store respective coupling coefficients of the layers other than the last layer. A weight memory stores weights of neurons of the last layer. Address converters carry out arithmetical operations to find out addresses of nets of the network whose coupling coefficients are significant. A table memory outputs a total coupling coefficient obtained by inter-multiplying the significant coupling coefficients read out from the coefficient memories of the layers. A cumulative operation unit performs cumulative additions of the product of the total coupling coefficient times the weight of the weight memory. Arithmetical operations are carried out only on particular nets with a significant coupling coefficient value. The speed of operation and recognition can be improved.
    • 在通过进行网络算术运算来识别和处理图像等的特征数据的多层神经网络中,特征数据存储器存储层的特征数据。 系数存储器存储不同于最后层的层的相应耦合系数。 体重记忆存储最后层的神经元的权重。 地址转换器进行算术运算,找出耦合系数很大的网络网络地址。 表存储器输出通过相互乘以从层的系数存储器读出的有效耦合系数而获得的总耦合系数。 累积操作单元执行总耦合系数乘以权重存储器的权重的乘积的累积加法。 仅对具有显着耦合系数值的特定网络进行算术运算。 可以提高运行和识别的速度。
    • 3. 发明授权
    • Neural network circuit for adaptively controlling the coupling of neurons
    • 用于自适应控制神经元耦合的神经网络电路
    • US5452402A
    • 1995-09-19
    • US155865
    • 1993-11-23
    • Shiro SakiyamaMasakatsu MaruyamaHiroyuki NakahiraToshiyuki KoudaSusumu Maruno
    • Shiro SakiyamaMasakatsu MaruyamaHiroyuki NakahiraToshiyuki KoudaSusumu Maruno
    • G06F15/18G06N3/04G06N3/08G06N99/00G06T7/00G06F15/00
    • G06K9/62G06N3/08G06N3/082
    • In a multi-layered neural network circuit provided with an input layer having input vectors, an intermediate layer having networks in tree-like structure whose outputs are necessarily determined by the values of the input vectors and whose number corresponds to the number of the input vectors of the input layer, and an output layer having plural output units for integrating all outputs of the intermediate layer, provided are learning-time memories for memorizing the numbers of times at learning in paths between the intermediate layer and the respective output units, threshold processing circuits for threshold-processing the outputs of the leaning-time memories, and connection control circuits to be controlled by the outputs of the threshold processing circuits for controlling connection of paths between the intermediate layer and the output units. The outputs of the intermediate layer connected by the connection control circuits are summed in each output unit. Thus, the neural network circuit for recognizing an image or the like can execute recognition and learning of data to be recognized at high speed with small circuit size, and the recognition accuracy for unlearned data is high.
    • 在设置有具有输入向量的输入层的多层神经网络电路中,具有树状结构的网络的中间层,其输出必须由输入向量的值决定,其数量对应于输入向量的数量 以及输出层,具有用于积分中间层的所有输出的多个输出单元,所述输出层是用于存储中间层和各个输出单元之间的路径中学习次数的学习时间存储器,阈值处理 用于对倾斜时间存储器的输出进行阈值处理的电路和由用于控制中间层和输出单元之间的路径连接的阈值处理电路的输出来控制的连接控制电路。 由连接控制电路连接的中间层的输出在每个输出单元中相加。 因此,用于识别图像等的神经网络电路可以以较小的电路尺寸执行高速识别的数据的识别和学习,并且未被读取的数据的识别精度高。
    • 5. 发明授权
    • Neural network for voice and pattern recognition
    • 用于语音和模式识别的神经网络
    • US6067536A
    • 2000-05-23
    • US864938
    • 1997-05-29
    • Masakatsu MaruyamaHiroyuki NakahiraMasaru FukudaShiro Sakiyama
    • Masakatsu MaruyamaHiroyuki NakahiraMasaru FukudaShiro Sakiyama
    • G06K9/66G06N3/063G06E1/00
    • G06K9/6267G06K9/00986G06N3/063
    • A neural network circuit for performing a processing of recognizing voices, images and the like comprises a weight memory for holding a lot of weight values (initial weight values) which correspond to a plurality of input terminals of each of a plurality of neurons forming a neural network and have been initially learned, and a difference value memory for storing difference values between the weight values of the weight memory and additionally learned weight values. The weight memory is formed by a ROM. The difference value memory is formed by a SRAM, for example. During operation of recognizing input data, the initial weight values of the weight memory and the difference values of the difference value memory are added together. The added weight values are used to calculate an output value of each neuron of an output layer. Accordingly, the initial weight values can be additionally learned at a high speed by existence of the difference value memory having a small capacity. Thus, new numerals, characters and the like can be recognized well without error.
    • 用于执行识别语音,图像等的处理的神经网络电路包括用于保存与形成神经元的多个神经元中的每一个的多个输入端相对应的大量权重值(初始权重值)的权重存储器 网络并且已经被初步了解,以及差值存储器,用于存储权重存储器的权重值和附加学习的权重值之间的差值。 重量存储器由ROM形成。 差值存储器例如由SRAM形成。 在识别输入数据的操作期间,将加权存储器的初始权重值和差值存储器的差值相加在一起。 加权重值用于计算输出层每个神经元的输出值。 因此,通过存在具有小容量的差值存储器,可以高速地附加地学习初始权重值。 因此,可以很好地识别新的数字,字符等。
    • 8. 发明申请
    • Bicyclic Pyrrole Derivatives
    • 双环吡咯衍生物
    • US20080318922A1
    • 2008-12-25
    • US11722037
    • 2005-12-21
    • Hiroyuki NakahiraHidenori KimuraHitoshi Hochigai
    • Hiroyuki NakahiraHidenori KimuraHitoshi Hochigai
    • A61K31/519C07D487/04A61K31/5377C07D401/04A61P3/10
    • C07D487/04
    • Compounds represented by the general formula (I), prodrugs thereof, or pharmaceutically acceptable salts of both are provided as compounds which have high DPP-IV inhibiting activity and are improved in safety, toxicity and so on: (I) wherein the solid line and dotted line between A1 and A2 represents a double bond (A1=A2) or the like; A1 is C(R4) or the like; A2 is nitrogen atom or the like; R1 is hydrogen atom, optionally substituted alkyl group, or the like; R2 is hydrogen atom, optionally substituted alkyl group, or the like; R3 is hydrogen atom, halogen atom, or the like; R4 is hydrogen atom, hydroxyl, halogen atom, or the like; and Y is a group represented by the general formula (A) or the like; (A) [wherein m1 is 0, 1, 2 or 3; and the group (A) may be freed from R6 or substituted with one or two R6's which are each independently halogen atom or the like.]
    • 提供由通式(I)表示的化合物,其前体药物或两者的药学上可接受的盐作为具有高DPP-IV抑制活性并且具有改善的安全性,毒性等的化合物:(I)其中实线和 A1和A2之间的虚线表示双键(A1 = A2)等; A1是C(R4)等; A2是氮原子等; R1是氢原子,任选取代的烷基等; R2是氢原子,任选取代的烷基等; R3是氢原子,卤素原子等; R4是氢原子,羟基,卤素原子等; Y为由通式(A)等表示的基团。 (A)[其中m1为0,1,2或3; 并且基团(A)可以被R6除去,或被一个或两个各自独立地为卤素原子的R6取代。
    • 10. 发明申请
    • Reproduced signal processor and reproduced signal processing method
    • 再现信号处理器和再生信号处理方法
    • US20050219985A1
    • 2005-10-06
    • US10513367
    • 2003-09-02
    • Hiroyuki Nakahira
    • Hiroyuki Nakahira
    • G11B20/10H03H17/02H03H17/06H03H21/00G11B5/09
    • G11B20/10009G11B20/10046G11B20/10481H03H17/06
    • As shown in FIG. 1, in the reproduced signal processing apparatus (100) of the present invention, the pattern predictor (103) predicts a predicted value which is a data sequence of a reproduced signal X and judges whether the predicted value matches a previously set specific pattern or not, and the adaptive equalizer (110) performs adaptive equalization on the reproduced signal X with timely updating the coefficients W of the digital filter according to the judgement result from the pattern predictor (103), and the selection circuit (104) outputs one of the output from the adaptive equalizer (110) and the predicted value as a waveform-equalized output Y on the basis of the judgement result. The reproduced signal processing apparatus (100) so constructed can realize optimal waveform equalization for coping with the non-linear distortion included in the reproduced signal.
    • 如图所示。 如图1所示,在本发明的再现信号处理装置(100)中,模式预测器(103)预测作为再现信号X的数据序列的预测值,并且判断预测值是否与预先设定的特定模式匹配 ,并且自适应均衡器(110)根据来自模式预测器(103)的判断结果及时更新数字滤波器的系数W,对再现信号X执行自适应均衡,并且选择电路(104)输出 根据判断结果,将自适应均衡器(110)的输出和预测值作为波形均衡输出Y输出。 这样构成的再现信号处理装置(100)可以实现最佳的波形均衡,以应对包含在再生信号中的非线性失真。