| 词汇 | example_english_neural-network |
| 释义 | Examples of neural networkThese examples are from corpora and from sources on the web. Any opinions in the examples do not represent the opinion of the Cambridge Dictionary editors or of Cambridge University Press or its licensors. There is a probability that the neuralnetwork misclassifies a grasp. If the neuralnetwork-based diagnosis fails, a case-base is used to realize a more specific diagnosis. The knowledge representation and reasoning of model-based and rule-based diagnoses are symbol-based, while those of neuralnetwork-based and case-based diagnoses are instance-based. The results obtained with the hybrid model are better than those using just a neuralnetwork to solve the problem, as shown in the table. I describe an example of a potential synergy based on studies of neuralnetwork pruning. This explains the criticism of neuralnetwork modeling and connectionism (p. 33 and other parts of the book). From a functional point of view, a single neuralnetwork may be considered sometimes as a pattern generating and/or a pattern recognizing device. To assess the influence of external and internal factors on the neuralnetwork accuracy, a three-phase experiment is created. A neuralnetwork approach to hippocampal function in classical conditioning. An artificial neuralnetwork constitutes the "brain" that controls the agent's behavior. The neuralnetwork could not predict test results when the interval exceeded 2 days. These investigations have examined clinical interventions for depression based upon a neuralnetwork model. A linear predictor model is developed and used as a benchmark for evaluating the performance of the neuralnetwork. By sampling the trained neuralnetwork, approximations of likelihoods were obtained for design variable values, and, consequently, constraint satisfaction problemsolving capability was achieved. This information will be used for formulation of the problem and will be input to the neuralnetwork as well. Tables 7.1 and 7.3 demonstrate that the proposed ' self-organizing neuralnetwork ' can be used effectively to model the acquisition process. Figures 6-7 show the results of a single link collision detection based on both the neuralnetwork and the model based approaches. In order to learn the double signature of the rectangular black border a neuralnetwork has been used. Then, the neuralnetwork learns to generalize the via point position. A neuralnetwork approach and a model based method are developed to detect the collision forces and disturbance torques on the joints of the manipulator. One solution to this problem is to utilize artificial neuralnetwork. Results can be used in the learning function of the neuralnetwork. On the other hand, the disassembly methods and the neuralnetwork-based approaches can cope with larger parts, but usually find local optimal solutions. Addressing these would take us too far afield, as would discussion of nonlinear dynamic of learning in a neuralnetwork. Other methods are learning control, neuralnetwork techniques18 and fuzzy control19,20 for robot force control. To overcome this difficulty, the paper proposes a force reflection gain-selecting algorithm based on neuralnetwork and fuzzy logic features. The "feedback-error-learning" approach was then used to design a neuralnetwork controller that learns the nonlinear inverse dynamics of the flexible-link system. The membership functions are automatically estimated through some probabilities and the neuralnetwork with a certain learning algorithm. A neuralnetwork has been created and trained to classify the digits. The model is based upon the use of a neuralnetwork, designed to represent the economy. The key to designing this controller is to include the base elastic deflections directly in the objective function of the neuralnetwork. When the tracker is not sure that a sequence is being repeated, or when the tracked sequence finishes, it returns control to the neuralnetwork. Then there is no need to use a different neuralnetwork for the different lookup tables. Sliding modes are used to determine best values for parameters in neuralnetwork learning rules, thereby robustness in learning control can be improved. On the other hand, people who have studied neuralnetwork models have tended to neglect symbol manipulation. Empirical studies on the speed of convergence of neuralnetwork training using genetic algorithms. The procedure of the proposed system design contains input data preparation and neuralnetwork training stages. An important example might be a neuralnetwork in which both synaptic and propagation delays contribute to its dynamic behavior. Each design agent includes a neuralnetwork to detect novelty, but different agents have different built-in preferences for novelty. His current work is developing speech and language front-ends to operational neuralnetwork systems for optical character recognition and vehicle tracking. An appropriate neuralnetwork is used to model the plant and is updated online. A back-propagation neuralnetwork predicts absorption maxima of chimeric human red0green visual pigments. The second nonlinear model is motivated by the neuralnetwork literature. Using agency-based modelling techniques, they have developed a computerised neuralnetwork for exploring the rationale for such inhibitory processes in the brain. When an appropriate landmark is found, the symbolic information it can contain (text or icons), is extracted and interpreted using a neuralnetwork. Finally, if a landmark is found, symbols are extracted and identified with a classical backpropagation neuralnetwork. Building successful neuralnetwork models is not an easy task. Specifically, the robustness analysis investigates the behavior of the neuralnetwork in the presence of modeling uncertainties prior to the occurrence of a fault. Classification of autoregressive spectral estimated signal patterns using an adaptive resonance theory neuralnetwork. This is achieved by setting the output weights of the neuralnetwork to zero. Training a neuralnetwork to achieve comparable error rate can take several days. In this paper, a neuralnetwork control with sliding mode is proposed to improve control system performance. Figure 1 depicts the neuralnetwork with its input and output signals. The system involves training a neuralnetwork in each robot to enable it to handle different generic types of static obstacles. Although these rules do provide transparency to the neuralnetwork, they do not provide comprehensibility. The function model of neuralnetwork can construct and train a neuralnetwork-based classifier for fault detection and diagnosis from the database or data warehouse. Their approach uses a neuralnetwork to learn appropriate simulation knowledge and builds probabilistic behavior models from that knowledge. However, the recent advent of neuralnetwork and connectionist modeling provides a theoretical tool for capturing complex dynamic interactions. A conventional neuralnetwork has limitations of computational power. Neuralnetwork development would directly support the ideas of dynamic orchestration, as outlined above through the use of object-oriented programming. Finally, the stability analysis examines the behavior of the neuralnetwork after the occurrence of a fault. Specifically, we develop methodologies to train a neuralnetwork to learn the inverse kinematics solution from a given forward kinematics of a manipulator. The approach taken in this paper is trying to solve this problem by using neuralnetwork learning of inverse kinematics of a redundant manipulator. With this methodological approach it is possible to determine which of the considered factors have the most effect on given neuralnetwork errors. The neuralnetwork of the basal ganglia as revealed by the study of synaptic connections of identified neurones. A customized neuralnetwork for sensor fusion in on-line monitoring of cutting tool wear. This tends to increase the computation time of the neuralnetwork, which may impede the on-line pattern recognition process. The development of an intelligent flexible and programmable vibratory bowl feeder incorporating neuralnetwork. The fuzzy rule block is the kernel of the whole fuzzy neuralnetwork model. This classification is not made by criteria of semantic description but by a trained neuralnetwork classifier without semantic explanation. Real-time supervised structure0parameter learning for fuzzy neuralnetwork. Figure 4 is an example of a neuralnetwork that works in this manner. The system described here achieves automated discrimination between different species by utilizing a novel time domain signal coding technique and an artificial neuralnetwork. Analytical studies based on bifurcation theory should clarify the possible operating modes of a given neuralnetwork. The fruit of this branch of investigation is neuralnetwork theory. And degrees of belief might be realized at different levels of activation in the appropriate neuralnetwork, for example. However, it is logical to give each, at any time, the computational abilities of a localist neuralnetwork. The following three chapters take us into the realm of neuralnetwork models. Disassembly sequence generation using a neuralnetwork approach. Neuralnetwork synthesis using cellular encoding and the genetic algorithm. Then, this possibly large number of samples is used to construct a global picture of the neuralnetwork behavior. In general, the choice of the architecture of a neuralnetwork is a complex problem that does not have a theoretical solution. The functions in the program tree specify how to develop the embryonic neuralnetwork into a full neuralnetwork. Another neuralnetwork was trained to estimate the value of critical variables when their measurements are inconsistent. The inputs of the controller neural net is the output of the industry or the simulated neuralnetwork. By allowing the neuralnetwork to escape from an attractor ruin via an unstable manifold such a process is possible. In the example, the system adjusts the weights throughout the stack of networks if a customer accepts or rejects the selected neuralnetwork location. Resemblance is no longer only a question of distances measured over a neuralnetwork, it also covers topological similarities. These examples are from corpora and from sources on the web. Any opinions in the examples do not represent the opinion of the Cambridge Dictionary editors or of Cambridge University Press or its licensors. |
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