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정 우철, 최 현구, 이 원홍, 최 정우

서강대학교 화학공학과

Simulation of color recognition using bioelectronic device

incorporated with neural network algorithm

Woo-Chul Jung, Hyun-Goo Choi, Won Hong Lee and Jeong-Woo Choi

Department of Chemical Engineering, Sogang University

Introduction

Neural network algorithm mimics the brain's own problem solving process.  Just as humans apply knowledge gained from past experience to new problems or environments, a neural network takes previously solved examples to build a system of neurons that makes new decisions, classifications, and forecasts.[1] Neural networks look for patterns in training sets of data, learn these patterns, and develop the ability to correctly classify new patterns or to make predictions. Neural networks excel at problem diagnosis, decision making, prediction, classification, and other problems where pattern recognition is important and precise computational answers are not required.

In this study, input layer, hidden layer and output layer were composed of 10, 10, 3 nodes, respectively(Figure. 1(a)). This network is trained to recognize color pattern. The extreme values are duplicated as validating examples in order to terminate the learning process as soon as they are correct. Photocurrent generated from device was used as real input data for learning neural network. The process begin by considering learning with a teacher, which is also referred to as the supervised learning.[2]

Materials and Methods

Using a 300W Xenon arc lamp with optically transparent color mask, the simple color was projected onto the biomolecular photoreceptor consisting of bR/Flavin complex LB films.[3] The response profiles used as inputs were obtained from bioelectronic deivce upon the different color projections, especially Red, Green and Blue colors. According to each property of band-pass filter, the output data were characterized and normalized by three basic color.

The network begins by finding linear relationships between the inputs and the output. Weight values are assigned to the links between the input and output neurons.  After those relationships are found, neurons are added to the hidden layer so that nonlinear relationships can be found. Input values in the first layer are multiplied by the weights and passed to the hidden layer. Neurons in the hidden layer produce outputs that are based upon the sum of weighted values passed to them. The hidden layer passes values to the output layer in the same fashion, and the output layer produces the desired results.

The network learns by adjusting the interconnection weights between layers. The answers which the network are producing are repeatedly compared with the desired answers, and each time the connecting weights are adjusted slightly in the direction of the desired answers (Figure. 1(b)). Additional hidden neurons are added as necessary to capture features in the data set.

Eventually, if the problem can be learned, a stable set of weights evolves and will produce good answers for all of the sample decisions or predictions. The real power of neural networks is evident when the trained network is able to produce good results for data which the network has never seen before.

In spites of there's a lot of effective parameter to decrease error, increasing  hidden layer can make it's network model poor or weak such as confusing or complexity on appropriate network. To optimize network architecture, the simulation is fulfilled by changing the number of hidden units from 10 to 20 instead of hidden layer.

(a)

(b)

Figure. 1 Neural Network

(a) Neural Architecture, (b) Supervised Learning

Results and Discussion

The basic building block of neural network technology is the simulated neuron. Independent neurons are of little use, however, unless they are interconnected in a network of neurons. The network processes a number of inputs from the environment to produce an output, the network's predictions. The neurons are connected by weights.

Neurons were grouped into layers by their connection to the environment. For example, if a neuron receives data from outside of the network, it is considered to be in the input layer. If a neuron contains the network's predictions, it is in the output layer. Neurons between the input and output layers are in the hidden layer, which serves as a feature detector.

Three kinds of color data were simulated with respect to many cases of hidden units. Here, 6 cases of hidden unit(even number) were chose. Networks have applied to chose hidden units. As a result, the optimized output can be achieved at the network with 18 hidden units. (Figure. 2)

(a)

(b)

Figure. 2 Learning according to hidden layer architecture

(a) Learning cycle within error tolerance 0.1

(b) Error trends of model with 18 hidden units (SSE)

(b)

(a)

(c)

Number of hidden units

Figure 3. Prediction of output with respect to Red, Green and Blue colors.

(a) Output of red signal, (b) Output of green signal, (c) Output of blue signal

In most situations, there is no way to determine the best number of hidden units without training several networks and estimating the generalization error of each. If neural network has few hidden units, it would get high training error and high generalization error due to underfitting and high statistical bias. On the other hand, if network has too many hidden units, it might get low training error but still have high generalization error due to overfitting and high variance.

There were remarkable results on how fast approximation error decreases as the number of hidden units increases. All simulated networks represented big difference in learning cycle from one another. The network with 18 hidden units showed better performance than others in predicting the output color(Red, Green, Blue), which converged to the desired error bound(0.1) with only 500 learning cycle. (Figure. 3) In addition, an approximate fit would result in a R-Squared value of 1. The network performance statistics is better known as R-Squared or the coefficient of multiple determination, a statistical indicator usually applied to multiple regression analysis.

[eqn: R^2 ~=1 - SSE over SS_rr]

where [eqn: SSE~=~ SUM (y- y')^2], [eqn: SS_rr~=~ SUM (y- overline y )^2], y is the actual value, y' is the predicted value of y, and [eqn: overline y] is the mean of the y value.

R-Squared is not the ultimate measure of whether network model is producing good results. It might provide additional information of the network by the number of answers within a certain percentage of the actual answer, the mean squared error between the actual answers and the predicted answers, the analysis of the actual versus predicted graph, etc. The success of the net depends more upon the problem and the field rather than a single statistical measure.

It is concluded that simulation process could be successfully applied to color recognition of the bioelectronic device. In order to prepare the more accurate and organized architecture, the optimization of the learning parameter, network architecture, R-square and the minimization of SSE(Sum of Square Error) or complex network such as mesh conditions should be required.

Acknowledgement

This work was supported by the Korean Ministry of Science and Technology under the Brain Science Research Program (00-J04-02-04-A-03, 2000).

References

[1] Simon Haykin., Neural networks: A Comprehensive Foundation (2nd Ed.), Prentice Hall, Singapore, 1999.

[2] De Wilde, Neural network models : theory and projects (2nd Ed.), Springer, London, 1997.

[3] H.G. Choi, Biomolecular Photoreceptor Consisting of Bacteriorhodopsin/Flavin Complex LB Films for Color Recognition, Ph.D. Thesis, Sogang University, 2000.