Сайт Информационных Технологий


Victor Tolstykh, Irina Tolstykh

St. Petersburg State Technical University.
Polytehnicheskaya 29, St.-Petersburg, 195251, Russia,

Аннотация – Обобщение искусственного нейрона заменой линейной свертки входного вектора с набором весовых коэффициентов на произвольную многопараметрическую функцию. Веса синапсов заменены на набор параметров, которые в данном случае являются не синаптическими коэффициентами, а внутренними параметрами тела нейрона. Обобщен алгоритм обучения обобщенного “smart” нейрона, позволяющий находить значения произвольного набора параметров, не связанного с размерностью входного вектора. Один “smart” нейрон достаточен для построения нелинейных многомерных зависимостей, ранее требовавших нейронных сетей со сложной топологией. Нейронная сеть является частным случаем “smart” нейрона. Нейронная сеть, состоящая из “smart” нейронов, определяет следующий уровень в иерархической структуре “искусственного интеллекта”.

1. Smart Neuron

The artificial neural networks are very popular for purpose of multi-dimensio-nal or complex data approximation. But attention of researches and users is mostly focused in the idea of elaboration and investigation of topology of sophis-ticated neuron networks. In this article we try to generalize the idea of simple single neuron to make one more “smart”.

Single neuron has simple construction. From technical point of view it is only a device for convolution of multi-dimensional vector with constant vector (co-vector) of weights. Mainly focus is in the neuron network topology because the Kholmogorov’s theorem provides a theoretical support of the idea.

Accordingly the theorem, we can make decomposition of any differentiable multidimensional function into the one-dimensional set of simple functions-neurons. It was very exciting idea and the realization of it gets us important applications. From mathematical point of view any network can be represent as a set of linear independent functions with free parameters-weights. Therefore, good making network is only subset of parameterized functions, which can approximate wide class of data.

From this it is obviously that a linear parameters-weights entry is very rigorous limitation. Alternative of this is making the network with more complex elements than only simplest artificial neurons. We named the neuron “Smart” Neuron, because this single neuron can approximate a multi-dimensional non-linear dependence instead the regular neuron, which can approximate only linear dependence. Thus it is a generalized idea of simple artificial neuron.

Using smart neurons we can make more powerful existing ordinal neural networks just only replacing original neurons by smart ones.

Definition. Smart Neuron (SN) is a device, which calculates value of multi-dimensional non-linear function.

The model of SN is any multi-parametric set of analytical differential functions.

No weights vector in the definition is required. The training process only interactively determines free coefficients of model inside the neuron.

2. Training algorithm

Theorem. Let be a differentiable by all parameters function of n variables . Let is unlimited training set. Then we can define parameters of the function iteratively using training process


where a is a small positive constant.

Initial value of W is arbitrary. In particular case if n=m and then formula (1) has conventional form [1,3]

Accordingly to the definition we can choose any set of functions whatever we want. The only approximate property is important. Here we have for example set of polynomial, rational functions, trigonometric functions and others. Even linear combination of neurons can be used. Thus SM can be conventional neuron sub-network also.

Dualism of SM: We can consider SN as a generalization of conventional neuron and as a generalization of whole network in the same time. It is good point for evaluation of hierarchical structure of future networks with the intelligence increasing. Anyway the combination of smart neurons in regular network seems to be optimal.

3. Using with Neural Network

The interpretation of conventional artificial neural networks can be as follow:

Let m is a number of elements on the input layer – dimension of input vector,

ni is a number of neurons on the ith hidden layer, n is a number of elements on the output layer – dimension of output vector.

We can interpret the scheme in term of multi-dimensional mappings


where every mapping gk is a transformation of input vector from layer k-1 to layer k.

Superposition is the multi-dimensional vector-function we are looking for. Every local function is a superposition of linear function with free parameters W and non-linear predefined function . is a scalar product (convolution) between input vector and weight vector, which is internal for every neuron. In term of interconnection of W and V we will name the neuron “linear neuron”. Certainly the generalization of idea of simple linear neuron is a free parameterized function .

This makes some difference between generalized artificial neuron and the biological neuron, because there are no weights in synapses and number of parameters does not depend from dimension of input space.


Fig.1. Conventional and Smart Neurons

Number of parameters and its interconnection with input vector is arbitrary and makes its purpose the better approximation of gk. That’s why we name the neuron “Smart”. Ordinary neuron can approximate the linear dependence if the area bounded by non-linear function . In contrary the SN can approximate any non-linear dependence if the internal model of SN generates a basis functions class.

4. Usage

Example: the polynomial is a basis for any infinite differentiable function on the bounded area. Generally we can use for this purpose the class of differential linear equation solutions. Class of rational functions includes the polynomial class and also generates basis function class for any differentiable function.

The whole network is represented by multi-dimensional vector-function . It is also a function with a set of free parameters of every neuron , where W is a summarized vector of all weights in the network. Of course the network can be also generalized as a one smart neuron.

In this point of view the main problem is how to enhance the training process for purposes of SN training. Obviously conventional back-propagation algorithm does not convenient.

Fig.2.Quasi-periodic function restoration

Example. Quasi-periodic function is a linear combination of periodic functions. We can approximate that sort of function by periodic using Fourier series.

Fig.3. Rational functions restoration

Example. The double peaks signals and several steps of their restoring by using only one neuron with internal function as a rational polynomial of the 4th power.

Usage Neural Network model as single Smart Neuron. In this case general formula does not exist. The main idea of neural network is in the simple linear decomposition of multidimensional function by a set of simple neurons:


These neurons are linear functions in the finite areas. For example it could be wavelet decomposition


where y i(x) is a wavelet function so-called “inverse of Mexican hat”.

Conventional neural network using “inverse of Mexican hat” is a powerful method for approximation of multi-dimensional dependences. But the only linear coefficients of “hats” in the formula are free. It is an excessive limitation. Using this way we need a lot of neurons even for simple dependence. Another trouble of flexibility we get from the model, because the result usually is a “black box”.

On the other hand the Smart Neuron allows use the models with arbitrary coefficients entry and it proves that this method includes all previous methods with a particular case as parameter-weight.


The neural network is a particular case of “smart” neuron. We can construct next level of hierarchy of neural networks using the set of generalized “smart” neurons in the network. It allows us to increase the “intellect” of artificial neural networks.


  1. Q.Zhang. Using wavelet network in non parametric estimation. IEEE Trans Neural Networks 8 (1997), 227-236.
  2. Y.Harcouss, J.Rousset, I.M.Collantes, M.Campocecchio. An accurate neural network model of FET for intermodulation and power analysis. 26th European Microwave Conf, Czech Republic, Sept.1996.
  3. B.Widrow, Winter and Baxter. Learning phenomena in layered neural network. In 1st Int. Conference Neural Nets, San Diego, volume 2, page 411. 1987.
  4. A.H.Zaabab, Q.J.Zhang, M.Nakhla. Analysis and optimization of microwave circuits and devices using neural network models. In IEEE int. Microwave Symp. Digest. San Diego, CA, 19994.
  5. D.Rumelhart, G.Hinton, R.Williams. Learning internal representations by error propagation. In Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Vol.1. 1986
  6. M.Ishikawa. A structural learning algorithm with forgetting of link weights. Electronical Lab., Tsucuba City, Japan. Tech. Rep. TR-90-7, 1990.
  7. P.B.L.Meijer. Fast and smooth highly nonlinear multidimensional table models for device modeling. IEEE Trans. Circuits Syst. 1, vol.37. 1990


Site of Information Technologies
Designed by