CN101846970A - Electric heating furnace device - Google Patents

Abstract

The invention discloses an electric heating furnace device and relates to an intelligent control method, in particular to an improved neural network control method. The invention provides an optimizing method of an electric heating furnace device by utilizing the improved neural network learning method aiming at the nonlinearity of a control object of the electric heating furnace device. An improved neural network adopts an improved weight value correcting method.

Description

Electric heating furnace device

Affiliated technical field

Patent of the present invention relates to a kind of intelligence control method, and particularly a kind of improved neural network control method is applied to the electric heating furnace device transformation.

Background technology

In the industrial control system, the traditional PID control method of the general employing of electric heating furnace device, this method has under specific applying working condition controls effect preferably, but because the parameter of controller is not easy to adjusting, when applying working condition changes, the control effect that can not obtain well.For the control of process temperature, because the complicacy of actual condition is difficult to accurately set up mathematics model.Because neural network has learning ability and the non-linear characteristic of approaching preferably, the controller existing certain research in theoretical and practical application based on neural network comprises that neural network is the decoupling controller on basis.Because the learning ability of neural network has very big influence to the decoupling performance of whole decoupling controller, therefore the present invention proposes a kind of improved network learning method.

The ultimate principle of BP learning algorithm is the gradient method of steepest descent, and its central idea is to adjust weights to make network total error minimum.Adopt the gradient search technology, so that the error mean square value minimum of the real output value of network and expectation.Network learning procedure is a kind of process of error back-propagating modified weight coefficient.

In general, learning rate is big more, and the change of weights is fierce more, and at the training initial stage, bigger learning rate is favourable to the quick decline of error, but has arrived certain phase, and big learning rate may cause vibration, energy function promptly occurs and neglect to rise and to fall suddenly or go up not down.So, speed of convergence and be the obvious deficiency of BP algorithm slowly to the dependence of algorithm convergence parameter.Numerous methods have proposed improvement project, below are a kind of algorithms that can take all factors into consideration speed of convergence and parameter robustness.

Summary of the invention

The present invention utilizes following improved network learning method, has proposed one group of injecting machine material tube heating system method.

The theme step of BP network calculations:

(a). put the initial value of each weights and threshold values

(p=1,2...Q) wherein p is a several layers, Q represents total number of plies

(b). input training sample (I _q, d _q), (p=1,2...M) wherein M represents input and output quantity, to each sample calculation output and weights correction

(c). the actual output x of each layer of computational grid ^p=f (s ^p)=f (w ^px ^P-1), f in the formula (*) is an activation function

(e) if the desired output that it is exported and each top-mould type is right is inconsistent, then its error signal is returned from the output terminal backpropagation, and in communication process, weighting coefficient is constantly revised, up to till obtaining needed expectation input value on the output layer neuron.After sample being finished the adjustment of network weight coefficient, send into another sample mode again to carrying out similar study, till finishing a training study.

Below utilize method of conjugate gradient to the weights correction:

Consider the quadratic form performance function

Its gradient is Its second order gradient is the Hessian matrix

So the change amount of gradient is

Δg[k]＝g[k+1]-g[k]＝(Qw[k+1]+b)-(Qw[k]+b)＝QΔw[k]＝α[k]Hp[k]

In the formula, α [k] is prolonging direction p[k constantly] search makes the minimum learning rate of performance function E (w)

For the quadratic form performance function, optimum learning rate is pressed following formula and is determined

So, according to conjugate condition, and because learning rate is a scalar, so α [k] p ^T[k] Hp[j]=Δ g ^T[k] p[j]=0.Conjugate condition just changes direction of search p[j into] with the change amount Δ g[k of gradient] quadrature, and irrelevant with the Hessian matrix.

Initial search direction p[0] can be arbitrarily, the 1st iteration direction p[1] as long as and Δ g[0] quadrature, begin follow-up direction p[k usually with direction of steepest descent] as long as and the change amount sequence of gradient Δ g[0], Δ g[1] ... Δ g[k-1] quadrature gets final product.A kind of concise and to the point method is to adopt iteration P[k+1]=β [k+1] P[k]-g[k+1]

Wherein: $\mathrm{\β}\left[k\right]=\frac{[1+w(k-1)]g^T\left[k\right]g\left[k\right]}{{[1-w(k-1)]g}^T[k-1]g[k-1]}$

$E\{w\left(k\right)+\frac{p^2\left[k\right]}{p[k-1]}\}{|}_{\mathrm{\α}\left[k\right]={\mathrm{\α}}^{*}\left[k\right]}=\min ,$ $w(k+1)=w\left(k\right)+\frac{p^2\left[k\right]}{p[k-1]}$

Description of drawings

Fig. 1 is the structural drawing that improves neural network in this method

Embodiment

The present invention utilizes improved network learning method, has proposed one group of electric heating furnace device remodeling method, and wherein improved neural network realizes according to the following steps:

(a). put the initial value of each weights and threshold values

(p=1,2...Q) wherein p is a several layers, Q represents total number of plies

(b). input training sample (I _q, d _q), (p=1,2...M) wherein M represents input and output quantity, and each sample is carried out (c)～(c) step

(c). the actual output x of each layer of computational grid ^p=f (s ^p)=f (w ^px ^P-1), in the formula, f (*) is an activation function

(d). compute gradient g (k) and gradient change amount Δ g[k]

(e). revise weights $w(k+1)=w\left(k\right)+\frac{p^2\left[k\right]}{p[k-1]}$

P[k wherein] be about w (k) sequence, β [k] sequence, g[k] function of sequence, as P[k+1]=β [k+1] P[k]-g[k+1]

(f). all samples in sample set have all experienced c～e step, promptly finish a cycle of training, calculation of performance indicators

$E\left(w\right)=\frac{1}{2}{w}^T\mathrm{Qw}+b^{T}w+c$

(g) if. the accuracy requirement of performance index cat family, i.e. E≤ε, training finishes so, otherwise forwards (b) to, continues next cycle of training.ε is little positive number, chooses according to actual conditions.

Wherein the computing method of β [k] are as follows: $\mathrm{\β}\left[k\right]=\frac{[1+w(k-1)]g^T\left[k\right]g\left[k\right]}{{[1-w(k-1)]g}^T[k-1]g[k-1]}$

Wherein activation function can adopt: trigonometric function, bipolarity function, piecewise function, sigmoid function, based on warping function of sigmoid function etc.

Described correction weights refer in particular to behind individual iterative computation several times, and the direction of search is re-set as gradient direction, again by (e) iteration.

Claims (4)

1. the technical characterictic of electric heating furnace device is:

The present invention utilizes following improved network learning method, has proposed one group of electric heating furnace device systems approach.

Described improved network learning method flow process is carried out in the following manner:

(a). put the initial value of each weights and threshold values

(p=1,2...Q) wherein p is a several layers, Q represents total number of plies

(b). input training sample (I _q, d _q), (p=1,2...M) wherein M represents input and output quantity, and each sample is carried out (c)～(e) step

(c). the actual output x of each layer of computational grid ^p=f (s ^p)=f (w ^px ^P-1), in the formula, f (*) is an activation function

(d). compute gradient g (k) and gradient change amount Δ g[k]

(e). revise weights

$w(k+1)=w\left(k\right)+\frac{p^2\left[k\right]}{p[k-1]}$

P[k wherein] be about w (k) sequence, β [k] sequence, g[k] function of sequence, as P[k+1]=β [k+1] P[k]-g[k+1]

(f). all samples in sample set have all experienced (c)～(e) step, promptly finish a cycle of training, calculation of performance indicators,

$E\left(w\right)=\frac{1}{2}{w}^T\mathrm{Qw}+b^{T}w+c$

(g) if. the accuracy requirement of performance index cat family, i.e. E≤ε, training finishes so, otherwise forwards (b) to, continues next cycle of training.ε is little positive number, chooses according to actual conditions.

2. according to claim item 1, the technical characterictic of described activation function is:

Activation function can adopt: trigonometric function, bipolarity function, piecewise function, sigmoid function, based on the warping function of sigmoid function, etc.

3. according to claim item 1, the technical characterictic of described correction weights is:

Described correction weights refer in particular to behind individual iterative computation several times, and the direction of search is re-set as gradient direction, again by (e) iteration.

4. according to claim item 1, the technical characterictic of described β [k] is:

$\mathrm{\β}\left[k\right]=\frac{[1+w(k-1)]g^T\left[k\right]g\left[k\right]}{[1-w(k-1)]g^T[k-1]g[k-1]}$

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