A NEURO-FUZZY EMBEDDED SYSTEM FOR INTELLIGENT

ENVIRONMENTS

Javier Echanobe, Ines del Campo and Guillermo Bosque

Department of Electronics, University of the Basque Country, Spain

Keywords:

Intelligent environments, Embedded systems, Neuro-Fuzzy, FPGA.

Abstract:

Intelligent Environments are endowed with a large number of non-intrusive, embeded electronic systems, such

as sensors, microprocessors, actuators, etc. These electronic systems must exhibit intelligent abilities in order

to learn and adapt from the users’ habits and preferences. In this paper, we propose a Neuro-Fuzzy electronic

embedded system to control several ambient parameters of an intelligent environment, such as temperature,

illumination, volume of the sound, and aroma. In particular the PWM-ANFIS model has been selected which

provides learning/adaptation features and also fuzzy reasoning. The system is implemented on a reconﬁgurable

device (i.e., FPGA) leading to a small, compact and very efﬁcient electronic system.

1 INTRODUCTION

An Intelligent Environment, also known as Ambi-

ent Intelligence Environment, is characterized by

its ubiquity, transparency and intelligence (ISTAG,

2001)(ESTO, 2003)(ISTAG, 2005). It must be ubiq-

uitous in the sense that the environment is endowed

with a multitude of embedded electronic systems such

as sensors, microprocessors, communication devices,

actuators, etc. It must be also transparent so the users

do not note the presence of the electronic infraestruc-

ture in question. Finally, the system requires intelli-

gent abililties in order to process - in a smart way - the

large amount of information coming from the sensors,

to be aware of the context, and also to learn and adapt

from the habits and necessities of the users.

Neuro-Fuzzy Systems are a class of systems pro-

vided with some intelligent features. In particular, the

Neuro-Fuzzy System model known as ANFIS (Adap-

tive Neuro-Fuzzy Inference System) (Jang, 1993) has

been widely used in different application scopes such

as Robotics, Control Systems, Automotive, Pattern

Recognition, etc. (Jang, 1993) (Jang and Sun, 1995),

providing quit good results. The main feature of

an ANFIS system relies on its ability to integrate in

an efﬁcient way knowledge representation, reasoning,

and learning. That is, on the one hand it is able to

describe a problem by means of linguistic rules of

the form ”IF-THEN” and to perform reasoning using

fuzzy logic. On the other hand, it also exhibit learn-

ing performances typical of Neural-Networks. Hence,

an ANFIS system can adapt its behaviour (i.e., its re-

sponse) from a set of learning samples in order to han-

dle new different siuations. Both reasoning and learn-

ing attributes constitute the powerful characteristics

of Neuro-Fuzzy systems to be used in Intelligent En-

vironments.

On the other hand, reconﬁgurable devices like FP-

GAs are a class of efﬁcient electronic devices which

are widely used in an increasing number of areas.

Moreover, they are usually the main component of

many embedded systems. They provide both high

performance because they are hardware based and

high ﬂexibility because they can be easily reconﬁg-

ured in order to modify or improve their functional-

ity. In addition, the increasing complexity and in-

tegration of electronic devices have led to powerful

new FPGA device families with a huge amount of re-

sources, able to accommodate all the components of a

typical embeddedsystem (e.g., processor cores, mem-

ory blocks, peripherals, dedicated hardware, etc.), on

a single chip, commonly referred to as system-on-

a-programable-chip (SOPC). Hence, FPGA are ap-

propiate devices to be used in Intelligent Environ-

ments where there could be a great number of such

systems integrated in all the space in a non-intrusive

way.

In this paper, we propose the development over

reconﬁgurable devices (i.e., FPGAs) of an embedded

electronic system for the intelligent control of an in-

559

Echanobe J., del Campo I. and Bosque G. (2009).

A NEURO-FUZZY EMBEDDED SYSTEM FOR INTELLIGENT ENVIRONMENTS.

In Proceedings of the International Joint Conference on Computational Intelligence, pages 559-564

DOI: 10.5220/0002321105590564

 SciTePress

habited environment. In particular, an ANFIS-like hy-

brid Neuro-Fuzzy architecture has been designed and

implemented. The system is provided with adaptation

and learning features so it can ﬁt the necessities and

preferences of the users of the environment, including

changes that can arise after a long period of time.

The rest of the paper is organised as follows: Sec-

tion 2 introduces the proposed ANFIS-like neuro-

fuzzy system. Section 3 shows an eﬁcient hetero-

geneous hardware-software architecture to implement

the PWM-ANFIS system. In Section 4, an embed-

ded system intended for an Intelligent Environment is

showed. Section 5 shows the implementation of the

system which has been carried out on a FPGA de-

vice. Some details about the HW/SW requirements

and also the performance of the system are presented

here. Finally, some conclusions and future works are

outlined.

2 ADAPTIVE NEURO-FUZZY

INFERENCE MODEL

An ANFIS system (Jang, 1993) is a Fuzzy Infer-

ence System whose parameters (membership func-

tions, strength of the rules, consequents, ...) are

trained by means of Neural Network algorithms. The

system can be viewed as a particular Neural Network

that is functionally equivalent to a Fuzzy Inference

System. Hence, it exhibits both the linguistic knowl-

edge representation of fuzzy systems and the learn-

ing abilities of Neural Networks. On the other hand,

note that as Neural Networks exhibit a notable par-

allelism in their structure, very efﬁcient implementa-

tions can be achieved for ANFIS systems by means

of hardware solutions (e.g. FPGAs based implemen-

tations (Omondi and Rajapakse, 2006)(del Campo

et al., 2008)). Let us describe the structure of an AN-

FIS system:

Consider a rule based n-input fuzzy system with

m antecedent functions per dimension. There are m

rules where the j-th rule can be expressed as:

: IF x

is A

and ...and x

is A

THEN f is

... j

where x = (x

, x

, . . . , x

), x∈ R

is the input vec-

tor; A

, . . . A

are linguistic labels associated with

the membership functions µ

) (i.e., antecedents of

the rules), y is the output variable, and c

... j

∈ R

is a crisp consequent (i.e. a singleton). If the center

of gravity defuzziﬁcation method is adopted, the out-

put of the system is given by (1) being w

= A

) ·

) · · · · · A

f =

∑

j=1

∑

j=1

(1)

This fuzzy system can be represented by a neu-

ral network (Figure 1) where the ﬁrst layer computes

the membership functions; the second layer computes

the values w

(1 ≤ j ≤ m

); the third layer normal-

izes these values; the fourth layer computes w

(1 ≤ j ≤ m

). Finally the output of the network is pro-

vided by the ﬁfth layer which aggregates the overall

output as the summation f =

∑

. It can be easily

shown that this network performs the same function

as the fuzzy system (Jang, 1993). Regarding the com-

putational cost, it is required 1 division, m

· (n + 1)

products, 2 · m

sums and m · n membership evalua-

tions to perform an inference.

Figure 1: ANFIS Network structure.

To train the above network, a hybrid algorithm has

been proposed (Jang, 1993). The algorithm is com-

posed of a forward pass which is carried out by a

Least Squares Estimator (LSE) process, followed by

a backward pass which is carried out by a back prop-

agation (BP) algorithm. The LSE computes the con-

sequents and the back propagation adjusts the param-

eters of the antecedents. Although the back propaga-

tion algorithm could train the network alone, the train-

ing process can be accelerated with the LSE. This is

possible due to the fact that the output of the network

is linear in the consequent parameters.

Due to the huge amount of information and the

large number of parameters that are involved in an

Intelligent Environment, we have used a modiﬁed

model called PWM-ANFIS (PieceWise Multilinear

ANFIS) which we have proposed (del Campo et al.,

2008) (Echanobe et al., 2008) to reduce the computa-

tional requirements in high dimensionality systems.

In particular, we have shown how, by introducing

some restrictions on the membership functions (i.e.,

antecedents), a much more simpliﬁed system can be

obtained with hardly any loss of the learning and ap-

proximation capabilities. These restrictions basically

involve the use of normalized triangular membership

IJCCI 2009 - International Joint Conference on Computational Intelligence

560

functions overlapped by pairs. As a result, it is only

required 2

(2n+ 1) + n products and 2

+ 2n sums to

perform an inference. As we can see, this cost is dras-

tically less than the initial one and it does not depend

on the number of membership functions m. More-

over, as n and m increase, the difference between the

two costs becomes huge.

3 HARDWARE/SOFTWARE

ARCHITECTURE

Neural Networks contain two types of algorithms: On

the one hand there are training algorithms (e.g. BP,

LSE) that exhibit high irregularity, require high preci-

sion arithmetics and demand high computational cost.

On the other hand, we have the computation of the

network itself, which provides the network output for

a given input (i.e., the inference of the rules in the

ANFIS). This process implies regular and repetitive

calculations which are suitable to be performedin par-

allel.

Taking into account these two different algorithms

we propose an heterogeneous HW/SW architecture

to implement the PWM-ANFIS (see Figure 2). HW

implementations can exploit the parallel nature of

the algorithms, therefore the computation of the net-

work output will be implemented in the HW parti-

tion. Moreover, as it is shown in Figure 2, many

PWM-ANFIS subsystems may be implemented all at

once because many parameters must be simultane-

ously controlled in an Intelligent Environment. On

the other hand, the training algorithms are imple-

mented clearly better in software and hence they will

be addressed in the microprocessor-based system. In

fact, an analysis in detail of the hybrid training al-

gorithm (LSE+BP) shows that a hardware implemen-

tation would be highly complex. The control of the

global process and the input-output data handling is

also carried out by this software partition. Although

all the process can be developpedin software, the ben-

eﬁts of the hardware parallelism are huge. For exam-

ple, if we observe the complexity expresions in the

previous section, we have that it is required 56 prod-

ucts and 14 sums to perform an inference in a 3-input

system. The hardware partition can perform all these

operations in parallel consuming many few clock cy-

cles whereas a microprocessor would spent at least 70

(56+14) cycles. Moreover, if several systems are im-

plemented simultaneously the difference is outstand-

ing. In next sections, we will show the implementa-

tion of 4 systems in the same FPGA.

In particular we will develop our implementation

over an FPGA device that integrates together with the

I/O DATA

uProcessor system

FPGA

Training

algorithms

Softcore

PLB (Peripheral Local Bus)

2 inputs

3 inputs

4 inputs

PWM-ANFIS HW blocks

i+1

n-1

Figure 2: Architecture of the proposed system.

reconﬁgurable hardware one (or more) micropocessor

core. Hence, the Software partition will be addressed

in this core while the HW partition will use the rest of

the FPGA. Therefore, all the system is integrated on

a single device with the corresponding advantages in

size, power, and cost.

4 DEVELOPED SYSTEM

An Intelligent Environment can be understood as a

collection of distributed devices (i.e., sensors and ac-

tuators) linked with one or more electronic control

units (ECUs) (Schoitsch and Skavhaugh, 2006) (i.e.,

microprocessors, microcontrollers, FPGAs, ...) en-

dowed with robust algorithms which implies ”smart”

or ”intelligence” capabilities. In this work, we have

carried out the design, implementation and veriﬁca-

tion of an embedded electronic system for one of this

ECUs, following the architecture described in the pre-

vious section. We assume that some ambiental pa-

rameters values are provided by external sensors and

that we can modify some others via actuators. In par-

ticular, we deal with 6 input parameters of a room

or salle: External Temperature, Internal occupancy

(i.e., persons inside the room), Internal Noise, Exter-

nal Light level, External Humidity and External Pol-

lution. The output variables of the system are: In-

ternal Temperature, Internal Light Level, Volume of

the Sound (if music is being heard) and Intensity of

an Air-Freshener or a Fragance. The control of these

four variables are achieved by 4 PWM-ANFIS sub-

systems whose dependence on the input variables is

showed in Table 1. Thus, we have two 2-input sub-

systems and two 3-input sub-systems. The choice of

these variables, their dependences and the behaviour

of the environment have been provided by an expert

from an architecture and environment-design com-

A NEURO-FUZZY EMBEDDED SYSTEM FOR INTELLIGENT ENVIRONMENTS

561

pany. In particular, this knowledge has been ex-

pressed by means of linguistic rules of the form ”IF-

THEN”. From this set of rules a Fuzzy System has

been built up and from it, a collection of input-output

data has been generated to train the PWM-ANFIS.

Table 1: Dependence between input and ouput environment

parameters in the 4 PWM-ANFIS Subsystems.

Subs.1 Subs.2 Subs.3 Subs.4

Inputs Temp. Light Volum. Aroma

Occupancy X X X

Ext. Light X X

Ext. Temp. X

Pollution X X

Int. noise X

Humidity X

In order to verify that the PWM-ANFIS system

preserves good approximation capabilities despite the

restrictions imposed on the antecedent functions, we

have compared the output surfaces obtained a) by the

PWM-ANFIS system and b) by using the target func-

tion derived from the set of linguistic rules (i.e., from

the constructed fuzzy system). Also the training error

curves have been represented to analyze the learning

process. Two of the four sub-systems described above

are showed here: A 2-input system and a 3-input sys-

tem. In the following section we present these results.

4.1 2-Input PWM-ANFIS

As an example of a 2-input system, we show here

the results obtained with the ﬁrst sub-system of Ta-

ble 1: the control of the internal Temperature with

respect to the occuppancy and to the External Light

Level. To train this system, 121 input-output data

pairs ((x

, x

), f) are collected by taking 11 equally

distributed values for both the Occupancy (x

) and

the External Light Level (x

), and computing the In-

ternal Temperature values ( f) by the fuzzy system

as we have already commented. The number of an-

tecedent functions per input variable has been set to

5 so we obtain a total of 25 rules and consequents.

Figure 3 shows the output surfaces for this example

(all the variables have been normalized to the interval

[0,1]): a) The target function to be approximated; b)

The approximated function obtained with the PWM-

ANFIS system once it has been trained. We can see

how the approximated surface is quite similar to the

target one. On the other hand, Figure 4 shows the re-

sults of the learning procedure. It can be seen how

the shape and position of the triangular membership

functions (antecedents) have been modiﬁed from an

initial symetric distribution to ﬁt the training data. On

the other hand the evolution of the error value - during

the training phase - between the PWM-ANFIS output

and the target function is shown. As we can see, a

very small value is obtained even after few iterations,

so the learning ability is still quite good.

Figure 3: Output surfaces for Internal Temperature y1 re-

garding the Occupancy level (x1) and the External Light

level(x2). Target surface (up) and PWM-ANFIS surface

(down).

4.2 3-Input PWM-ANFIS

To illustrate a 3-input system we show here the sub-

system used to control the Internal Illumination with

respect to the External Illumination (x

) , External

Temperature (x

), and the External Pollution (x

Again, 11 equally distributed values are taken for ev-

ery input variable so a total of 1331(11x11x11)input-

output data pairs are collected to train the system.

In addition, ﬁve antecedents are set per dimension,

leading to a total of 125 rules. Figure 5 shows the

ouput surfaces for a ﬁxed value of the External Pol-

lution (i.e., so it can be represented). Again we can

see that both surfaces are quite similar so the system

also presents here good approximation performances.

With regard to the learning process, the results are

IJCCI 2009 - International Joint Conference on Computational Intelligence

562

Figure 4: Example 1: triangular antecedents after training

(up) and training error evolution (down).

also satisfactory and again a very small error value is

obtained after a small number of iterations (see Figure

6).

These two examples show the good approxima-

tion capability of the PWM-ANFIS. Obviously, the

generic ANFIS (i.e., unrestricted gaussian member-

ship functions) provides, general speaking, better ap-

proximation capability. However, the degree of ap-

proximation can be arbitrarily set in our system by

taking a higher number of antecedents per dimension,

m. Moreover, higher m does not increase the com-

plexity of the system as it was shown in Section 2.

5 SYSTEM IMPLEMENTATION

The system has been implemented in a XILINX’s

FPGA, in particular over a XC2VP30 device from

the VIRTEX-II Pro familly. Such a device is an in-

termediate one in the VIRTEX-II Pro familly and

integrates a PowerPC-based microprocessor system,

13.696 Logic Blocks (Slices), 136 multipliers, em-

bedded memory and some other resources. In addi-

tion, the ISE and EDK design tools, also from XIL-

!" !#

Figure 5: Output surfaces for Internal Illumination y2 re-

garding the External Illumination (x3) and the External

Temperature (x4), for a ﬁxed pollution value. Target sur-

face (up) and PWM-ANFIS surface (down).

INX, have been used to design, synthetise, simulate

and verify the implementation. The device resources

used by the implementation are the following: On the

one hand, each one of the two 2-input PWM-ANFIS

subsystems requires 137 Slices (1%) and 10 multi-

pliers (7%). On the other hand, each one of the two

3-input PWM-ANFIS subsystems requires 828 Slices

(6 %) and 35 multipliers (25%). The complete sys-

tem - including the HW/SW interface and the RAM

memory for the software partition - makes use of

1375 Slices (10%), 40 multipliers (29%) and 64 RAM

blocks (47%). The operation frequency of the system

is 100MHz both for the microprocessor and for the

clock of the hardware partition. Moreover, due to the

high parallel degree achievedfor the hardware design,

the evaluation of every PWM-ANFIS (i.e., the infer-

ence of the rules for a given input) is achieved in just

seven clock cycles (70ns). If these inferences were

performed solely in software it would be necessary at

least 196 cycles (152 products and 44 sums), as it is

derived from expresions in Section 2.

Concerning the precision of the computations, the

system provides its responses in a 32-bit signed data

A NEURO-FUZZY EMBEDDED SYSTEM FOR INTELLIGENT ENVIRONMENTS

563

Figure 6: Example 2: triangular antecedents after training

(up) and training error evolution (down).

format which is sufﬁcient precision for this kind of

application.

6 CONCLUSIONS

An efﬁcient Embedded Electronic System for Intelli-

gent Environments has been developped. The system

is intended for the control of some environment pa-

rameters inside a room such as temperature, illumina-

tion etc. The system is based on a Neuro-Fuzzymodel

which provides features such as learning, adapting

and fuzzy information processing. In particular a

PWM-ANFIS Neuro-Fuzzy model has been adopted

which is a computational-efﬁcient version of the well

known ANFIS model. First, some numerical simula-

tions were carried out in order to validate the model,

and the results obtained show an appropiate response.

Finally, an efﬁcient heterogeneous HW/SW imple-

mentation has been realised and tested. Training al-

gorithm is excuted in software due to its irregularity

and the required precission while the neural network

computation is addressed in hardware to exploit its in-

herent parallelism. This implementation is performed

over an FPGA device (i.e., a XILINX’s VIRTEX-II

Pro) and integrates in the same device four PWM-

ANFIS models, each one controllingone environment

parameter. It also integrates a microproccesor sub-

system which executes the learning procedure and the

input-output data handling. The whole design works

at a frequency of 100MHz and all the four PWM-

ANFIS systems perform simultaneously the rule in-

ferences in just 70ns. To improve even more the ef-

ﬁciency of the proposed system, future work will be

devoted to make use of the Dynamic Partial Recon-

ﬁguration techniques so that the size and the power

consumption of the systems can be optimized.

ACKNOWLEDGEMENTS

This work was supported in part by the Basque Coun-

try Government under Grants GIC07/138-IT-353-07

and S-PE08UN49.

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