RECURRENT NEURAL NETWORK

from polynomial time of computation. These results aresummarized in the following theorem.Theorem 2: (a) For any language L, there exists someRNN[R] that decides L in exponential time.(b) Let L be some language. Then L ∈ P/poly if and onlyif L is decidable in polynomial time by some RNN[R].III. EVOL[r]

Edith Cowan UniversityResearch OnlineECU Publications2005An analogue recurrent neural networks fortrajectory learning and other industrial applicationsGanesh KothapalliEdith Cowan UniversityThis conference paper was originally published as: Kothapalli, G. (2005). An analogue recurre[r]

computations. But this classical computational approach is inherently restrictive, especially when it refers to bio-inspiredcomplex information processing systems. Indeed, in the brain (or in organic life in general), previous experience must affectthe perception of future inputs, and older memories[r]

284 IEEE TRANSACTIONS ON ENERGY CONVERSION, VOL. 18, NO. 2, JUNE 2003Neural Network-Based Modeling and ParameterIdentification of Switched Reluctance MotorsWenzhe Lu, Student Member, IEEE, Ali Keyhani, Fellow, IEEE, and Abbas Fardoun, Member, IEEEAbstract—Phase windings of switched rel[r]

patterns and sends a reinforcement signal to the learning system. The aim of learning is to adjustthe mean and the standard deviation to increase the probability of producing the optimal real valuefor each input pattern.A special group of dynamic connectionist approaches is the methods that use the[r]

cies, modeling has to be done very accurately,and the operating conditions cannot vary ar-bitrarily.REFERENCESGil, P., J. Henriques, A. Dourado and H. Duarte-Ramos (1999). Non-Linear Predictive ControlBased on a Recurrent Neural Network. In:ERUDIT Conference.Haley, P., D. Solowa[r]

w(k) and n(k)isan i.i.d. Gaussian noise vector. A zero-mean initialisation of model (10.10) is assumed(E[˜w(k)] = 0). This model covers most of the learning algorithms employed, be theylinear or nonlinear. For instance, the momentum algorithm models the weight updateas an AR process. In addition, le[r]

from polynomial time of computation. These results aresummarized in the following theorem.Theorem 2: (a) For any language L, there exists someRNN[R] that decides L in exponential time.(b) Let L be some language. Then L ∈ P/poly if and onlyif L is decidable in polynomial time by some RNN[R].III. EVOL[r]

4NGD (a standard nonlinear gradient descent) and NNGD algorithms for a colouredinput from AR channel (9.17). The slope of the logistic function was β = 4, whichpartly coincides with the linear curve y = x. The NNGD algorithm for a feedfor-ward dynamical neuron clearly outperforms the other employed[r]

of weighted interconnections, the concept of neural networks is fully exploited andmore powerful nonlinear predictors may ensue. For the purpose of prediction, memorystages may be introduced at the input or within the network. The most powerfulapproach is to introduce feedback and to u[r]

ponential in n before being capable of providing thesame output as N . In the proof of Proposition 2, theeffectivity of the two simulations that are describeddepend on the complexity of the synaptic configura-tions N (t) of N as well as on the complexity of theadvice function α(n) of M .Secondly, it[r]

the power of the continuum cannot be overcome byincorporating some further possibilities of synapticevolution in the model.To summarize, the possibility of synaptic evolu-tion in a basic first-order interactive rate neural modelprovides an alternative and equivalent way to the con-sideration o[r]

Corresponding author: Dr. Jérémie Cabessa, Grenoble Institut des Neurosciences (GIN), INSERM, UMR_S 836, Equipe 7, Université JosephFourier, Grenoble, France, La Tronche BP 170, F-38042 Grenoble Cedex 9, France. Fax: +33-456-520369, E-mail: [jcabessa, avilla]@nhrg.orgReceived: April 23, 2010; Revise[r]

typical neurogram after training the network with one stimulus(Fig. 6A,C) or two stimuli (Fig. 6B,D) in presence of 0 (“con-trol”) or 1 Hz Poisson noise. With PSD alone, training withoutrandom spikes (Fig. 6, rate ϭ 0) resulted in a small degree of jitterof the neural trajectories; the[r]

Disturbances BP LM RPROP ANFIS Neural-Genetic Sags 94.2 100 99.5 94.0 85.8 Swells 92.2 100 99.8 94.8 88.8 Interruptions 99.9 100 100 100 83.2 Oscillations 89.9 100 99.6 88.8 98.5 Mean 94.1 100 99.7 94.4 89.1 Table 3. Performance of intelligent systems with pre-processing stage Comparing Tabl[r]

704 5 Safety and Risk i n Engineering Designclose to reality) and associative (i.e. include typ ical profiles) but not descriptive. Ex-amining the artificial neural network itself only shows meaningless numeric values.The ANN model is fundamentally a black box. On the other hand, being c[r]

302 IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART B: CYBERNETICS, VOL. 31, NO. 3, JUNE 2001Neural Network Approaches to DynamicCollision-Free Trajectory GenerationSimon X. Yang, Member, IEEE, and Max Meng, Member, IEEEAbstract—In this paper, dynamic collision-free trajectoryg[r]

11 Using Neural Networks in HYSYSOther Possible Investigations Try changing one of the manipulated variables outside the training range. What happens? If the Neural Network is switched on, what happens when a variable which is not a manipulated variable is changed? For example,[r]

Điển hình là, hàm số chuyển được chọn do nhà thiết kế và khi đó các tham số _ w _và _b_ sẽ được điều chỉnh nhờ một số quy tắc học tập để mối quan hệ đầu ra/đầu vào nơron đạt được một số [r]

they encounter the same or similar data, they are able to give correct or approximate result. Artificial neuron, based on sum input and transfer function, computes output values. The following figure shows an artificial neuron: Fig. 5. Artificial neuron Neuron Network Applied to Video Encod[r]