Classifications

• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
  • G06N3/00—Computing arrangements based on biological models
  • G06N3/02—Neural networks
  • G06N3/08—Learning methods
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
  • G06N3/00—Computing arrangements based on biological models
  • G06N3/02—Neural networks
  • G06N3/04—Architecture, e.g. interconnection topology
  • G06N3/0499—Feedforward networks
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
  • G06N3/00—Computing arrangements based on biological models
  • G06N3/02—Neural networks
  • G06N3/08—Learning methods
  • G06N3/09—Supervised learning

Definitions

• the invention relates to a method for training an artificial neural network and computer program products.
• the method relates to training an artificial neural network having at least one hidden layer with tributary neurons and an output layer with output neurons.
• the networks used are massively parallel structures for modeling arbitrary functional relationships. For this they are offered training data that represent the relationships to be modeled using examples. During training, the internal parameters of the neural networks, such as their synaptic weights, are adjusted by training processes to produce the desired response to the input data. This training is called supervised learning.
• CONFIRMATION COPY For this purpose, the errors of the output neurons are propagated backwards into the network (backpropagation). Using various processes (gradient descent, heuristic methods such as particle swarm optimization or evolution method), the synaptic weights of all neurons in the network are then changed so that the neural network approximates the desired functionality as precisely as possible.
• topology refers to the structure of the network. Neurons can be arranged in consecutive layers. For example, in a network with a single trainable neuron layer, one speaks of a single-layer network. The last layer of the network, whose neuron output is usually the only one visible outside the network, is called the output layer. Layers in front of it are accordingly called hidden layers.
• the inventive method is suitable for neural feed forward networks of any topology having at least one layer with feeder neurons and an output layer with output neurons.
• the described learning methods serve to cause a neural network to generate associated output patterns for particular input patterns. For this purpose, the network is trained or adapted.
• the training of artificial neural networks, that is estimating the parameters contained in the model usually leads to high-dimensional nonlinear optimization problems.
• the object of the invention is to further develop a method for training an artificial neural network in such a way that response values with minimal deviation from the desired output values are provided at predefined input values in the shortest possible time.
• the upstream neurons generate multilevel nonlinear computations of the input values and the intermediate values of other neurons.
• the task of the tributary neurons is to create a suitable internal representation of the functionality to be learned in a high-dimensional space.
• the task of the output neurons is to examine the offer of the feeder neuron and to determine the most suitable selection of non-linear allocation results. [17] Therefore, these two classes of neurons can be adapted differently and it has surprisingly been found that the time required for training an artificial neural network can be significantly reduced if only the output neurons are adapted.
• the method is based on a new interpretation of the mode of action of feed-forward networks and is essentially based on two process steps: a) Create suitable internal representations of the functionality to be trained. b) Choose an optimal selection from the offer of pre-calculated outputs of the feeder neurons.
• a feed-forward network is interpreted as a series connection of two subnetworks.
• the first part contains all the neurons except the output neurons. These neurons are initialized with random synaptic weights, random transfer functions, and random network topology, and are not altered at any stage of the adaptation. Therefore, they also generate only random nonlinear billing of the offered input information.
• the second part contains only the output neurons. These are connected according to the predetermined network topology with the first part of the network synaptic weights.
• weights are adapted to the task.
• This is preferably done with a tichonov-regularized regression between the random allocations (the intermediate result offer of the first subnet) and the necessary activation of the output neurons.
• the synaptic weights of the output neurons therefore select, according to the invention, from the random offer of the first subnetwork preferably in only one computation step, ie not iteratively and not with methods of gradient descent, the optimal synaptic weights of the output layer.
• a network can learn by: developing new connections, deleting existing connections, changing the weighting, adjusting the thresholds of the neurons, adding or deleting neurons.
• the learning behavior changes as the activation function of the neurons changes or the learning rate of the network changes.
• the synaptic weights of the output neurons be determined to adapt the output neurons.
• a commonly performed adaptation of the feeder neurons, preferably by adaptation of their synaptic weights, is not necessary according to the invention.
• the synaptic weights of the output neurons will be determined based on the values of those tributary neurons that are directly connected to the output neurons and the default output values.
• An advantageous method provides that the output neurons are adapted with fewer than five adaptation steps, preferably only one step.
• the invention relates to a method for controlling a system in which the future behavior of observable quantities forms the basis for the control function and artificial neural network is trained as described above.
• a compute rogramm with compute rogrammcodeschn to carry out the described method makes it possible to execute the process as a program on a computer.
• Such a computer program product can also be stored on a computer-readable data memory.
• FIG. 1 shows a highly abstracted scheme of an artificial neural network with several levels and feed-forward property
• Figure 2 is a diagram of an artificial neuron.
• the artificial neural network (1) shown in Figure 1 consists of 5 neurons (2, 3, 4, 5 and 6), of which the neurons (2, 3, 4) are arranged as a hidden layer and represent feeder neurons, while the neurons (5, 6) represent output neurons as the output layer.
• the input values (7, 8, 9) are assigned to the feeder neurons (2, 3, 4) and the output neurons (5, 6) are assigned output values (10, 11).
• the difference between the response (12) of the output neuron (5) and the output value (10), as well as the difference between the response (13) of the output neuron (6) and the output value (11), is referred to as an output error.
• the artificial neuron scheme shown in Figure 2 shows how inputs (14, 15, 16, 17) result in a response (18).
• the inputs (xj, x 2 , x 3, x n) are evaluated via weights (19) and a corresponding transfer function (20) leads to an activation (21).
• An activation function (22) with a threshold value (23) leads to an initial value and thus to a response (18), [45] Since the weighting (19) has the strongest influence on the response (18) of the neurons (2 to 6), the training process will be described below exclusively with regard to an adaptation of the weights of the network (1).
• the synaptic weights of all output neurons are determined by a ticho- nov regularized regression process between inverted predefined output values (10, 1 1) and those pre-calculation values of the tributary neurons (2, 3, 4) directly connected to the output neurons (5, 6) ) are connected. [49] If the desired approximation target is reached, ie if the output error is smaller than a set upper limit, the method ends here.
• the method according to the invention allows training within a few seconds or minutes.
• the method described thus makes it possible to greatly reduce the time required for a given artificial neural network.
• the network can be chosen large enough to achieve the desired quality of the results.
• the short training period opens up the use of artificial neural networks in less powerful computers, especially smartphones.
• Smartphones can thus be continuously trained during their use, after a training phase to provide the user information itself, which he retrieves regularly. If, for example, the user can display special stock market data daily via an application, these stock market data can be automatically displayed to the user during any use of the smartphone without the user first activating the application and retrieving his data.

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• 2012
  • 2012-06-06 DE DE102012011194A patent/DE102012011194A1/de not_active Withdrawn
• 2013
  • 2013-04-18 WO PCT/DE2013/000205 patent/WO2013182176A1/fr not_active Ceased

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