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1. 发明公开

EP1279109A1 APPARATUS AND METHOD FOR FUZZY ANALYSIS OF STATISTICAL EVIDENCE 审中-公开
标题翻译： DEVICE AND METHOD FOR统计证据的模糊分析
公开(公告)号：EP1279109A1
公开(公告)日：2003-01-29
申请号：EP01932506.7
申请日：2001-03-15
申请人： Chen, Yuan Yan
发明人： Chen, Yuan Yan
IPC分类号： G06F15/18
CPC分类号： G06K9/6278 , G06N3/0436
摘要： An apparatus and method for performing parallel distributed processing are disclosed. A plurality of nodes are connected with weight connections (figures 1 and 12). The weight connections are updated based on a likelihood function of the associated nodes (30). Also, inputs to nodes are aggregated using t-norm (306) or t-conorm functions, with outputs representing the possibility and belief measures (308).

2. 发明公开

EP1211636A1 Filtering device and method for reducing noise in electrical signals, in particular acoustic signals and images 审中-公开
标题翻译：过滤器装置和方法，用于以电信号降低噪音，特别是声信号和图像
公开(公告)号：EP1211636A1
公开(公告)日：2002-06-05
申请号：EP00830782.9
申请日：2000-11-29
申请人： STMicroelectronics S.r.l.
发明人： Poluzzi, Rinaldo , Mione, Cristoforo , Savi, Alberto
IPC分类号： G06N3/04
CPC分类号： G06K9/0051 , G06N3/0436
摘要： The filtering device (80) comprises a neuro-fuzzy filter (1; 80) and implements a moving-average filtering technique in which the weights for final reconstruction of the signal ( oL 3 ( i )) are calculated in a neuro-fuzzy network (3) according to specific fuzzy rules. The fuzzy rules operate on three signal features ( X 1( i ), X 2( i ), X 3( i )) for each input sample ( e ( i )). The signal features are correlated to the position of the sample in the considered sample window, to the difference between a sample and the sample at the center of the window, and to the difference between a sample and the average of the samples in the window. The filter device for the analysis of a voice signal comprises a bank of neuro-fuzzy filters (86, 87). The signal is split into a number of sub-bands, according to wavelet theory, using a bank of analysis filters including a pair of FIR QMFs ( H 0 , H 1 ) and a pair of downsamplers (85, 86); each sub-band signal is filtered by a neuro-fuzzy filter (86, 87), and then the various sub-bands are reconstructed by a bank of synthesis filters including a pair of upsamplers (88, 89), a pair of FIR QMFs ( G 0 , G 1 ), and an adder node (92).
摘要翻译：过滤装置（80）包括一个神经模糊过滤器（1; 80），并实现移动平均滤波技术，其中用于信号的最终的重建（OL3（i））的权重的神经模糊网络计算（ 3）雅丁具体模糊规则。模糊规则对三个信号特征（X1（i）中，X2（i）中，X3（i））的每一个输入样本（E（i））的操作。所述信号特征是相关的，以在所考虑的样品窗口中的样本的位置，到样品，并在该窗口的中心的样本之间的差值，以及在样品和样品中的窗口的平均之间的差。用于语音信号的分析的过滤器装置包括神经模糊滤波器（86，87）的一组。的信号被分成多个子频带，gemäß的小波理论，使用分析滤波器包括一对FIR QMFs（H0，H1）的一组和一对向下取样器（85，86）; 每个子带信号由神经模糊滤波器（86，87）过滤，并通过合成滤波器包括一对的银行，则各个子带重构上采样器（88，89），一对FIR QMFs的（G0，G1），以及加法器节点（92）。

3. 发明授权

EP0811198B1 NEURAL NETWORK 失效
标题翻译：神经网络
公开(公告)号：EP0811198B1
公开(公告)日：2000-03-29
申请号：EP96903134.3
申请日：1996-02-23
申请人： Osmetech PLC
发明人： PERSAUD, Krishna, Chandra
IPC分类号： G06F15/80
CPC分类号： G06K9/00536 , G06N3/0436
摘要： There is disclosed a pattern identifying neural network comprising at least an input and an output layer, the output layer having a plurality of principal nodes, each principal node trained to recognise a different class of patterns, and at least one fuzzy node trained to recognise all classes of patterns recognised by the principal nodes but with thresholds set at levels higher than the corresponding threshold levels in the principal nodes.

4. 发明公开

EP0956530A1 VERFAHREN ZUR TRANSFORMATION EINER ZUR NACHBILDUNG EINES TECHNISCHEN PROZESSES DIENENDEN FUZZY-LOGIK IN EIN NEURONALES NETZ 失效
标题翻译：处理用于在重新制定技术程序的变换DERIVE从该模糊神经网络中的
公开(公告)号：EP0956530A1
公开(公告)日：1999-11-17
申请号：EP98909297.0
申请日：1998-01-29
申请人： SIEMENS AKTIENGESELLSCHAFT
发明人： HOFFMANN, Wolfgang , SCHWULERA, Erik
IPC分类号： G06N3
CPC分类号： G06N3/0436
摘要： The invention relates to a method for transforming a fuzzy logic into a neural network. According to said method, sigmoid functions are linked together to simulate membership functions in such a way that retransformation of said neural network into a fuzzy logic remains possible even after optimization of the neural network. An advantage of the method provided by the invention is that a fuzzy logic can be transformed, particularly component by component, into a neural network and that said network can then be optimized in its entirety, i.e. all components combined. The fact that the trained neural network can be retransformed makes it possible ultimately to obtain an optimized fuzzy logic. In this way, it is possible to use fuzzy system software in an advantageous manner, in particular standardized fuzzy system software, to describe the optimized fuzzy logic.

5. 发明公开

EP0813127A3 Inference rule determining method and inference device 失效
标题翻译：一种用于确定推理规则和推理方法
公开(公告)号：EP0813127A3
公开(公告)日：1998-05-06
申请号：EP97113852
申请日：1989-05-19
申请人： MATSUSHITA ELECTRIC IND CO LTD
发明人： TAKAGI HIDEYUKI , HAYASHI ISAO
IPC分类号： G05B13/02 , G06N3/04 , G06N5/04 , G06N7/02 , G06F7/00
CPC分类号： G06N3/0436 , G05B13/0285 , G06N5/048 , G06N7/023 , Y10S706/90

6. 发明授权

EP0591259B1 VERFAHREN ZUR VERARBEITUNG VON UNSICHERHEITEN VON EINGANGSDATEN IN NEURONALEN NETZWERKEN 失效
标题翻译：方法输入数据的神经网络处理的不确定性。
公开(公告)号：EP0591259B1
公开(公告)日：1995-08-30
申请号：EP92911631.7
申请日：1992-06-16
申请人： SIEMENS AKTIENGESELLSCHAFT
发明人： KÄMMERER, Bernhard
IPC分类号： G06F15/80
CPC分类号： G06N3/0436
摘要： In many applications, the input signals in neuronal networks are beset by considerable uncertainties. For this reason, training data are often not representative enough for the test phase data. A method of processing uncertainties in neuronal networks is proposed to solve this problem. Modified input signals are calculated from the input signals of neurons by linearly combining the input signals with neutral values. The coefficients of this linear combination are measures of the certainty of the input signals.

7. 发明公开

EP0591259A1 VERFAHREN ZUR VERARBEITUNG VON UNSICHERHEITEN VON EINGANGSDATEN IN NEURONALEN NETZWERKEN 失效
标题翻译：方法输入数据的神经网络处理的不确定性。
公开(公告)号：EP0591259A1
公开(公告)日：1994-04-13
申请号：EP92911631.0
申请日：1992-06-16
申请人： SIEMENS AKTIENGESELLSCHAFT
发明人： KÄMMERER, Bernhard
IPC分类号： G06F15 , G06N3
CPC分类号： G06N3/0436
摘要： Les signaux d'entrée dans des réseaux neuroniques sont entachés d'incertitutes considérables dans le cas de nombreuses applications. Pour cette raison, les données d'apprentissage ne sont souvent pas assez représentatives pour les données de la phase de test. Afin de résoudre ce problème, un procédé de traitement d'incertitutes dans des réseaux neuronaux consiste à calculer des signaux d'entrée modifiés à partir des signaux d'entrée des neurones, en combinant linéairement les signaux d'entrée avec des valeurs neutres. Les coefficients de cette combinaison linéaire représentent des mesures de la certitude des signaux d'entrée.

8. 发明公开

EP0527567A2 A method of controlling a controlled object, and a control system for such a method 失效
标题翻译：用于控制该方法要被控制的对象和控制系统的方法。
公开(公告)号：EP0527567A2
公开(公告)日：1993-02-17
申请号：EP92306772.2
申请日：1992-07-23
申请人： HITACHI, LTD.
发明人： Koharagi, Haruo , Thara, Kazuo , Ishii, Yoshitarou , Suka, Hisao , Kawamata, Mitsuhisa , Azima, Toshiyuki
IPC分类号： G05B13/02 , G05B15/02 , G06F15/00 , A47L9/28 , A47L9/00
CPC分类号： D06F33/02 , A47L9/2821 , A47L9/2826 , A47L9/2831 , A47L9/2847 , A47L9/2894 , A47L15/0018 , A47L15/46 , D06F2202/02 , D06F2202/065 , G05B13/0285 , G06N3/0436
摘要： In order to control a controlled object (1), such as a vacuum cleaner or washing machine signals are derived from a single sensor (4 or 5), which measures a single property of the controlled object (1), and are the signals used by a characteristic amount sampling unit (8) to derive a plurality of control characteristics. These are then processed by a neural network (15) to derive a basic control signal. The characteristics, or further characteristics derived from a further sensor (4) and a local condition detector are analysed by a fuzzy logic system (16) and used to modify the basis control signal. In other arrangements, the fuzzy logic is applied (60A, 60B, 60C, 60D) to each characteristic before the characteristics are applied to the neural network (15). Because the calculation time needed by the neural network (15) is long, it is possible to detect (50) if one of the characteristics has a particular volume (is within a predetermined range). Then, a fixed pattern may be derived, modified only by the fuzzy logic (16).
摘要翻译：为了控制控制对象（1）：如真空吸尘器或洗衣机信号是从一个单一的传感器导出（4或5），其测量受控对象（1）的单一特性，及所使用的信号由特征量取样单元（8）来推导的控制特性的多个。然后将这些由神经网络（15）处理，以导出的基本控制信号。从另外的传感器（4）和一个本地状态检测器导出的特征，或进一步的特征是由模糊逻辑系统（16）分析并用于修改基本控制信号。在其它布置中，所述特征施加到神经网络（15）前的模糊逻辑应用（60A，60B，60C，60D），以每个特征。因为由神经网络（15）所需要的计算时间长，所以能够检测（50），如果特征之一具有特定的体积（在预定范围内）。然后，一个固定的图案可以被衍生，仅由模糊逻辑（16）修改。

9. 发明公开

EP0521643A1 Method of automated learning, an apparatus therefor, and a system incorporating such an apparatus 失效
标题翻译： Methode und Apparatfürautomatisiertes Lernen und ein System，das einen solchen Apparatenthält。
公开(公告)号：EP0521643A1
公开(公告)日：1993-01-07
申请号：EP92305720.2
申请日：1992-06-22
申请人： HITACHI, LTD.
发明人： Enbutsu, Ichiro, Ishinazaka Apt.554 , Baba, Kenzi , Hara, Naoki , Yoda, Mikio , Watanabe, Shoji , Yahagi, Hayao
IPC分类号： G05B13/02 , G06F15/18 , G06F9/44
CPC分类号： G06N5/025 , G05B13/0285 , G06N3/0436 , Y10S706/90
摘要： In order to speed up, and simplify, automated learning of rules by a neural network making use of fuzzy logic, data (120) from a system is analyzed by a teaching data creation means (140). This groups the data into clusters and then selects a representative data item from each group for subsequent analysis. The selected data items are passed to a rule extraction means (180). This investigates relationships between the data items, to derive rules, but eliminates rules which have only an insignificant effect on the system. The result are candidate rules which are stored in a first rule base (200). The candidate rules are then compared with rules in a second rule base (240) to check for duplication and/or contradiction. Only those rules which are not duplicated and not contradictory are stored in the second rule base (240). Hence, when fuzzy inference is used to control the system on the basis of rules in the second rule base (240), only valid rules which provide a significant effect on the system are used.
摘要翻译：为了加速和简化由使用模糊逻辑的神经网络自动学习规则，由教学数据创建装置（140）分析来自系统的数据（120）。这将数据分组成簇，然后从每个组中选择一个代表性的数据项进行后续分析。所选择的数据项被传递到规则提取装置（180）。这调查数据项之间的关系，导出规则，但消除对系统影响不大的规则。结果是存储在第一规则库（200）中的候选规则。然后将候选规则与第二规则库（240）中的规则进行比较，以检查重复和/或矛盾。只有那些不重复而不矛盾的规则被存储在第二规则库（240）中。因此，当使用模糊推理来基于第二规则库（240）中的规则来控制系统时，仅使用对系统产生重大影响的有效规则。

10. 发明公开

EP2732408A1 DETECTION AND CLASSIFICATION OF PROCESS FLAWS USING FUZZY LOGIC 审中-公开
标题翻译：检测和工艺缺陷分类研究模糊逻辑
公开(公告)号：EP2732408A1
公开(公告)日：2014-05-21
申请号：EP12747951.7
申请日：2012-07-13
申请人： Saudi Arabian Oil Company
发明人： ALMUBARAK, Yousef, Husain
IPC分类号： G06N5/04 , G06N3/04 , G06N7/02
CPC分类号： G06N5/048 , G06N3/0436 , G06N7/02
摘要： A fuzzy logic controller for a distributed control system that monitors a large electrical machine in order to detect and identify faults. Variables to be monitored by the fuzzy logic controller include oil pressure, oil temperature, and other critical variables that are used under classical logic to trip the electrical machine offline. After the input and output membership functions are identified, and a rule set is defined, the fuzzy logic controller fuzzifies the monitored variables to the input membership functions, determines an antecedent truth value, and implicates the antecedent truth value onto the output membership function, establishing a fuzzy output set. Where multiple output fuzzy sets are to be combined, they are amalgamated. The output fuzzy set or amalgamated combined output fuzzy set is then converted to a crisp value.

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