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Applied Neural Networks for Digital Signal Processing

with DSC TMS320F28335

Jan Michalík, VŠB - Technical Univerzity of Ostrava (20.07.2009, prof. Pavel Brandštetter, VŠB - Technical University of Ostrava)

Abstract

The paper presents a description of the neural networks typical for the digital signal processing. It puts emphasis on advantages and disadvantages towards classic methods in processing of the digital signals. The work introduces types of the neural networks which are frequently used. An inseparable part of the paper are applications these networks in basic concept of structures for the DSP. This paper describes problems in data flow-processing which were designed. The next part deals with the experiments with the starter kit eZdsp TMS320F28335 which contains floating-point digital signal processor. The special low-pass filter for the signal reconstruction is included. The experimental results with the kit and the wave graphs are included too.

1. Introduction

The technical neural networks are not meanwhile relatively explores a field in digital signal processing. Robustness these problems always offer hidden possibilities in this way a science.

It has been oftentimes published mathematical derivation the model of a neuron [1]. This paper directs at basic idea of principles, capabilities and function of given types of the synthetic neural networks.

Typical exponents are the feedforward and the self-organizing nets for the nets of the digital signal processing [2]. The use of these applications is very wide in the field.

The applied neural networks enable to realize so many problems in DSP. One of typical example can be some digital filter. This problem is possible to resolve in Matlab software because it offers the neural networks toolbox. It allows to program it means to design and train the nets. The neural network toolbox contains the feedforward nets with backpropagation process training, the self-organizing nets and also the adaptive linear network architecture with adaptive training. This fact is on simulative level only.

Support of the starter kit eZdsp TMS320F28335 enables the Matlab/Simulink which is contained in the Matlab software suite. The TMS320F28335 is floating-point digital signal controller works better than the F2812 in meaning of processing of the applied neural networks [4].

2. Why Using of Neural Networks

The theory of the neural networks is established on information from neurophysiology. The idea of the synthetic neural networks has been deduced basis an analogy with the biological neural networks. The neurons of the biological neural networks specialize on long evolution to receiving, processing, preservation, transmission and using information. The synthetic neural networks try to imitate function of the human brain which has capability to learn, it can process in parallel some information. It works as an associative memory between vectors of input and output variables.

Fig.2.1. Neural Network as Data Processor.

The conventional systems work sequentially according to given and exact algorithm. The neural networks work in parallel without beforehand given algorithm. Those are the main different between both of them. Their activity is based on training process which the neural networks progressively adapt to the resolve of a problem.

The synthetic neural networks can imitate

behavior of the human brain in those aspects:

• The skills are collected in teaching process. • The synaptic connections are used for storing these skills.

• The information is processed in parallel. The synthetic neural networks are very different

than another, today known approaches.

XI International PhD Workshop OWD 2009, 17–20 October 2009

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3. Applied Neural Networks for

Structures of Digital Signal

Processing

The feedforward neural networks are the most frequently use type of the nets in signal processing. The easiest use of these nets as the data processor is shown on fig. 3.1. A input vector of the neural networks is the vector which make a part of the signal { }nx . It is a FIR nonlinear filter in essence.

The nets realize a nonlinear dependence of the actual

sample ny on the vector [ ]1 1, ,...,T

n n n n Nx x x− − +=x .

Application determines if the net will be not variable with set the synaptic weights according to some rules or the net will learn with a signal flow. We take a note the principle of the neural network enables to use the vector output.

Fig.3.1. Nonrecursive Signal Processing by

Feedforward Network

Thus it is possible to create a nonlinear adaptive which correspond with a classical concept in principle. You can see that system in block diagram onfig. 3.2.

Fig.3.2. Adaptive Nonlinear Filter with Training

of Neural Network

The neural networks concept is possible to generalize to an example of the recursive filter IIR type according to fig. 3.3. It is necessary to alert on probable problems with the filter stability.

Fig.3.3. Recursive Neural Filter

That naturally takes the use in fact that the net realizes the vector function of the vector argument in stative representation.

4. Data Flow-Processing Design The data flow-process diagram was designed for

processing of a data which will be concretized in future. The input part of diagram is connects to block of the data gathering of function description. Data will be obtained from some device or measuring card. It means the signals which create a training set. It is possible to use a mathematical function description for testing. This set is trained in Matlab software using the neural networks toolbox. This toolbox enables a lot of possibilities for working with.

Fig.4.1. Data Flow-Process Diagram

The kit eZdsp F28335 is a forward-looking solution supported of Matlab/Simulink [5]. The task is implemented on the kit which realizes a concrete problem. The data flow-process diagram is shown above.

5. Experimental Results

with EZdsp TMS320F28335

The experimental results are shown on figures below. The basic signal flow-process diagram is on fig. 5.1. The diagram deals with flowing-process tested signal from the powerful function/arbitrary waveform generator Agilent 33220A. This function generator can generate many functions like noise or arbitrary signal which is controllable. This signal is connected to the support board with the kit. The kit has on board 12-bit ADC which gets samples from signal. The kit communicates to the TLV5614C DAC directly via the SPI interface.

Fig.5.1. Basic Signal Flow-Process Diagram for Experiments

It means the signal streams through from input to output towards low-pass filter and oscilloscope. Magnitudes and power spectrums of tested signal are shown on fig. 5.2. This shows the different between

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Fig.5.2. Magnitudes and Power Spectrums of Tested Signal {ch1: uIN = 8.3 V, ch2: uOUT = 2.7 V, chA: FFT of ch1, chB: FFT of ch2}

both signals. It sees it is more the spectral lines on channel B than on channel A. It is logical because noise of the kit and sampling products are added. The low-pass filter reconstructs output signal and smooth its waveform. The special low-pass filter in SMT is shown below.

Fig.5.3. Low-Pass Filter Solution

6. Conclusion

The target of this paper is giving information about my work. It was described the synthetic neural networks for signal processing and my experimental results with the kit eZdsp TMS320F28335. The basic signal flow-process is pivotal diagram which describes how to process some digital signal. The experimental results were done.

7. Acknowledgement

This research was supervised by Prof. Ing. Pavel Brandštetter, CSc. We would like also to express thanks to Department of electronics, FEECS, VŠB-TU Ostrava. The work was financed and supported by the Internal Grant Agency IGA 7/2009.

8. Bibliography And Authors

Bibliography

[1] Haykin, S.: Neural Networks: A Comprenhensive Foundation. 2nd edn. Prentice-Hall, Inc. Upper Saddle River, NJ, (1999), USA. ISBN 0-13-273350-1.

[2] Jan, J.: Číslicová filtrace, analýza a restaurace signálů. 1st edn. Vysoké učení technické v Brně (1997). ISBN 80-214-0816-2.

[3] Balara, D., Timko, J., Žilková J.: Aplikácie umelých neuronových sietí v elektrických pohonoch. 1st edn. Calypso, s.r.o. Košice (2002). ISBN 80-85723-27-1.

[4] Škuta, O.: Modified Concepts of the Artificial Neural Network Architecture in the Modern Control of Electrical Drives. Doctoral thesis. Ostrava (2008).

[5] Hudeček, P., Michalík, J., Pumr, J., Sobek, M., Vaněk, J.: Aplikace řídicích systémů s DSC. Sborník perspektivy elektroniky (2009). ISBN 978-80-254-4052-0.

Author:

Ing. Jan Michalík VŠB-Technical University of Ostrava 17. listopadu 15/21/72 708 33 Ostrava - Poruba tel. (420) 597 321 432 fax (420) 596 994 050

email: jan.michalik@vsb.cz