48 results about "Hierarchical neural network" patented technology

An approach for phoneme recognition is described. A sequence of intermediate output posterior vectors is generated from an input sequence of cepstral features using a first layer perceptron. The intermediate output posterior vectors are then downsampled to form a reduced input set of intermediate posterior vectors for a second layer perceptron. A sequence of final posterior vectors is generated from the reduced input set of intermediate posterior vectors using the second layer perceptron. Then the final posterior vectors are decoded to determine an output recognized phoneme sequence representative of the input sequence of cepstral features.

A parallel processing device that computes a hierarchical neural network, the parallel processing device includes: a plurality of units that are identified by characteristic unit numbers that are predetermined identification numbers, respectively; a distribution control section that, in response to input as an input value of an output value outputted from one of the plurality of units through a unit output bus, outputs control data including the input value inputted and a selection unit number that is an identification number to select one unit among the plurality of units to the plurality of units through the unit input bus; and a common storage section that stores in advance coupling weights in a plurality of layers of the hierarchical neural network, the coupling weights being shared by plural ones of the plurality of units. Each of the units includes: a data input section that receives control data as an input from the distribution control section through the unit input bus; a unit number match judgment section that judges as to whether a selection unit number included in the control data inputted in the data input section matches the characteristic unit number; a unit processing section that, based on an input value included in the control data inputted in the data input section, computes by a computing method predetermined for each of the units; and a data output section that, when the unit number match judgment section provides a judgment result indicating matching, outputs a computation result computed by the unit processing section as the output value to the distribution control section through the unit output bus, wherein, based on the coupling weights stored in the common weight storage section, the unit processing section executes computation in a forward direction that is a direction from an input layer to an output layer in the hierarchical neural network, and executes computation in a backward direction that is a direction from the output layer to the input layer, thereby updating the coupling weights.

A system and method for emotion-enhanced natural speech using dilated convolutional neural networks, wherein an audio processing server receives a raw audio waveform from a dilated convolutional artificial neural network, associates text-based emotion content markers with portions of the raw audio waveform to produce an emotion-enhanced audio waveform, and provides the emotion-enhanced audio waveform to the dilated convolutional artificial neural network for use as a new input data set.

An output value of neuron within an objective layer of a hierarchical neural network is computed. The data of the output value of neuron is stored in a memory only if the output value of neuron is greater than or equal to a predetermined value by referring to the computed output value of neuron within the objective layer. When the data of the output value of neuron on a former layer of objective layer is read from the memory, the data having a predetermined value is read, instead of the data of the output value of neuron not stored in the memory.

Generating loosely coupled parts by forming couplings between partial nodes in a hierarchical neural network in accordance with a check matrix of an error correcting code.

A connected layer feature is generated by connecting outputs of a plurality of layers of a hierarchical neural network obtained by processing an input image using the hierarchical neural network. An attribute score map representing an attribute of each region of the input image is generated for each attribute using the connected layer feature. A recognition result for a recognition target is generated and output by integrating the generated attribute score maps for respective attributes.

The invention discloses a training method of an image classification network, an image classification method and device and a server. The training method comprises the following steps: preparing a data set of pictures with labels as input in advance; Constructing corresponding hierarchical neural network structures according to different classification levels; And carrying out hierarchical training on each hierarchical neural network structure to obtain a parent class corresponding to the maximum probability value and probability values of the input pictures under the parent class belonging todifferent subclasses. According to the method and the device, the technical problem of overfitting caused by redundancy of a full connection layer due to very large data set classification data is solved. Through the training method provided by the invention, the phenomena of low network training speed and over-fitting of the network caused by too many full connection layer parameters are solved.According to the image classification method provided by the invention, hierarchical training is adopted, so that a subclass classification result can be more accurately obtained on a classificationresult of a parent class, and accurate classification is realized.

The invention provides a pipeline leakage detection method based on a hierarchical neural network. The neural network is layered, a rejection threshold and a rejection interval of each layer of neural network are set, a test sample is input into each layer of neural network in the leakage detection process, if one layer of neural network judges the category of the test sample, the classification process is finished, and if the layer of neural network rejects the test sample, the test sample is input to the next layer of neural network till the test sample obtains a category label. The method can effectively judge whether an existing pipeline leaks or not, accuracy of pipeline leakage detection is improved, and detection precision is good.

A domain linker region is predicted by inputting an amino-acid sequence of a protein whose structure is unknown in a hierarchical neural network having identified and learned the domain linker region. Also, the sequence characteristics of the linker domain is identified by a statistical method, and by combining the result with the secondary structure predicting method, a domain linker predicting method for an amino-acid sequence whose structure is unknown was constructed.

The invention discloses a text representation method and device based on a hierarchical neural network. The method comprises: converting each word forming a sentence into a vector; Inputting vectors corresponding to all words in the sentence into a neural network for aggregation, and outputting sentence representation corresponding to the sentence; Inputting all the sentence representations into aneural network to be aggregated, and generating document representations corresponding to all the sentence representations; And converting the document representation into a document classification vector through a full connection network, and obtaining prediction probability distribution of document classification based on the document classification vector. According to the method and a device,A hierarchical mechanism is introduced into a neural network model to solve a document representation problem for text classification; Interoperability of different tasks is better improved, a hierarchical neural system structure is fused into a neural network method, a new neural network model based on layering is caused, accuracy, performance and the like are obviously superior to those of an existing neural network model, and consumption is lower.

An artificial neural network for detecting and identifying errors in digital circuits is provided. Data from digital circuits are received and organized into current data set patterns by a supervisory control and data acquisition system. The supervisory control and data acquisition system transmits the current data set patterns to an artificial neural network error detection module. The artificial neural network error detection module compares the actual output of each current data set pattern to a calculated output for a corresponding stored data set pattern. The artificial neural network error detection module determines whether a match condition exists for the comparison. The artificial neural network error detection module outputs the results of the determination to a user interface.

The invention discloses a sea surface target one-dimensional range profile noise reduction convolutional neural network identification method, and belongs to the field of radar signal processing. Aiming at a low signal-to-noise ratio condition, original HRRP data is reasonably pre-processed to construct multiple types of sea surface target data sets under different signal-to-noise ratio conditions, a one-dimensional noise reduction convolutional neural network is constructed by using a deep learning technology, the signal-to-noise ratio of the low signal-to-noise ratio data is improved on the basis of keeping the high signal-to-noise ratio data free of fluctuation, and the residual structure of a convolutional neural network is utilized to reduce the learning burden of the deep neural network, so that an intelligent sea surface target classification and recognition model integrating noise reduction and classification is constructed, the recognition accuracy of the sea surface target is improved, the sea surface target recognition performance under the condition of low signal-to-noise ratio is improved, the classification and identification capability of the sea radar in a complex sea surface environment is enhanced, and the method has popularization and application values.

The invention discloses a hierarchical neural network query recommendation method and device. The hierarchical neural network query recommendation method comprises the steps of: performing query recommendation on a hierarchical neural network; establishing two neural networks, namely a session layer neural network for modeling the short-term query record of the user and a user layer neural networkfor modeling the long-term query record of the user, wherein the state vector of the session layer neural network at the current moment is used as the input of the user layer neural network at the current moment, and the state vector of the user layer neural network at the current moment is used as the input of the session layer neural network at the next moment; and outputting query recommendation content for a query session according to the session layer neural network and the user layer neural network. According to the scheme provided by the invention, the query recommendation efficiency and accuracy can be improved.

The invention provides a security multicast communication method based on a chaotic neural network. The security multicast communication method is characterized by comprising the steps of combining a symmetric cryptographic algorithm based on the chaotic neural network and a centralized key management protocol LKH, and applying the combination to multicast communication, wherein according to the chaotic neural network, the chaos theory is introduced into a neural network to enable an artificial neural network to have abundant non-linear dynamic characteristics and high computational complexity, and chaotic characteristics can be achieved through multiple neural network models, such as a disperse Hopfield neural network having the chaotic characteristics after improvement; according to the symmetric cryptographic algorithm based on the chaotic neural network, output of concurrent LFSRs is taken as input of the disperse Hopfield neural network having the chaotic characteristics, and random selection is performed on pseudorandom sequences generated by the LFSRs by means of the non-linear dynamic characteristics and the chaotic characteristics of the neural network. Therefore, in a secret key of the symmetric algorithm (as specified in the specification), T0 is a connection weight singular matrix, H is a circulant matrix, and H' is a transposed matrix of H.