Ahmad Lotfi

Smart homes for the elderly dementia sufferers: identification and prediction of abnormal behaviour

In this paper, we have described a solution for supporting independent living of the elderly by means of equipping their home with a simple sensor network to monitor their behaviour. Standard home automation sensors including movement sensors and door entry point sensors are used. By monitoring the sensor data, important information regarding any anomalous behaviour will be identified. Different ways of visualizing large sensor data sets and representing them in a format suitable for clustering the abnormalities are also investigated. In the latter part of this paper, recurrent neural networks are used to predict the future values of the activities for each sensor. The predicted values are used to inform the caregiver in case anomalous behaviour is predicted in the near future. Data collection, classification and prediction are investigated in real home environments with elderly occupants suffering from dementia.

Learning fuzzy inference systems using an adaptive membership function scheme

An adaptive membership function scheme for general additive fuzzy systems is proposed in this paper. The proposed scheme can adapt a proper membership function for any nonlinear input-output mapping, based upon a minimum number of rules and an initial approximate membership function. This parameter adjustment procedure is performed by computing the error between the actual and the desired decision surface. Using the proposed adaptive scheme for fuzzy system, the number of rules can be minimized. Nonlinear function approximation and truck backer-upper control system are employed to demonstrate the viability of the proposed method.

A new approach to adaptive fuzzy control: the controller output error method

The controller output error method (COEM) is introduced and applied to the design of adaptive fuzzy control systems. The method employs a gradient descent algorithm to minimize a cost function which is based on the error at the controller output. This contrasts with more conventional methods which use the error at the plant output. The cost function is minimized by adapting some or all of the parameters of the fuzzy controller. The proposed adaptive fuzzy controller is applied to the adaptive control of a nonlinear plant and is shown to be capable of providing good overall system performance.

Occupancy monitoring in intelligent environment through integrated wireless localizing aggents

The application of wireless localizing agents in the occupancy detection of a single-occupant ambient intelligent environment in the presence of visitors is addressed in this paper. A wireless sensor network constructed from sensory agents of PIR motion detection sensors and door contact sensors is employed to collect the occupancy data from different areas in the ambient intelligent environment. Additionally, an RSSI detection capability is integrated to create wireless localizing agents along with tagging the occupant as a mobile node to distinguish him/her from other occupants or visitors. It is shown that by using a wireless sensor network of localizing agents for the occupancy detection of the tagged occupant, the redundancy and noise in the occupancy signal is reduced; hence, the efficiency of occupancy detection in different areas of the environment is increased.

Matrix formulation of fuzzy rule-based systems

In this paper, a matrix formulation of fuzzy rule based systems is introduced. A gradient descent training algorithm for the determination of the unknown parameters can also be expressed in a matrix form for various adaptive fuzzy networks. When converting a rule-based system to the proposed matrix formulation, only three sets of linear/nonlinear equations are required instead of set of rules and an inference mechanism. There are a number of advantages which the matrix formulation has compared with the linguistic approach. Firstly, it obviates the differences among the various architectures; and secondly, it is much easier to organize data in the implementation or simulation of the fuzzy system. The formulation will be illustrated by a number of examples.

Interpretation preservation of adaptive fuzzy inference systems

The membership functions of an adaptive fuzzy inference system, during the adaptation process, may lose the meaning which was initially assigned to them. In this paper, the concept of rough sets is used to propose a constraint training algorithm. The proposed algorithm maintains the interpretation of the adaptive fuzzy inference systems during the training. The constraints on membership functions are implemented by means of hard or soft limit bounds on the updating parameters of membership functions. An example to illustrate the algorithm is included.

Behavioural pattern identification and prediction in intelligent environments

In this paper, the application of soft computing techniques in prediction of an occupants behaviour in an inhabited intelligent environment is addressed. In this research, daily activities of elderly people who live in their own homes suffering from dementia are studied. Occupancy sensors are used to extract the movement patterns of the occupant. The occupancy data is then converted into temporal sequences of activities which are eventually used to predict the occupant behaviour. To build the prediction model, different dynamic recurrent neural networks are investigated. Recurrent neural networks have shown a great ability in finding the temporal relationships of input patterns. The experimental results show that non-linear autoregressive network with exogenous inputs model correctly extracts the long term prediction patterns of the occupant and outperformed the Elman network. The results presented here are validated using data generated from a simulator and real environments.

Comments on "Functional equivalence between radial basis function networks and fuzzy inference systems" [with reply]

The above paper claims that under a set of minor restrictions radial basis function networks and fuzzy inference systems are functionally equivalent. The purpose of this letter is to show that this set of restrictions is incomplete and that, when it is completed, the said functional equivalence applies only to a small range of fuzzy inference systems. In addition, a modified set of restrictions is proposed which is applicable for a much wider range of fuzzy inference systems.

Soft computing applications in dynamic model identification of polymer extrusion process

Abstract This paper proposes the applications of soft computing to deal with the constraints in conventional modelling techniques of the dynamic extrusion process. The proposed technique increases the efficiency in utilising the available information during the model identification. The resultant model can be classified as a ‘grey-box model’ or has been termed as a ‘semi-physical model’ in the context. The extrusion process contains a number of parameters that are sensitive to the operating environment. Fuzzy rule-based system (FRBS) is introduced into the analytical model of extrusion by means of sub-models to approximate those operational-sensitive parameters. In drawing an optimal structure for each sub-model, a hybrid algorithm of genetic algorithm with fuzzy system (GA-fuzzy) has been implemented. The sub-models obtained show advantages such as linguistic interpretability, simpler rule-base and less membership functions (MFs). The developed model is adaptive with its learning ability through the steepest decent error back-propagation algorithm. This ability might help to minimise the deviation of the model prediction when the operational-sensitive parameters adapt to the changing operating environment in the real situation. The model is first evaluated through simulations on the consistency of model prediction with the theoretical analysis. Then, the usefulness of adaptive sub-models during the operation is further explored in existence of prediction error.

Importance of membership functions: a comparative study on different learning methods for fuzzy inference systems

This paper investigates different adaptive structures for fuzzy inference systems. We examine the effect of membership functions on reasoning process when the number of rules is fixed. Three commonly used membership function shapes have been employed in this study. It has been shown that membership functions have the dominant effect on reasoning process rather than number of rules or inference mechanism. We compare our adaptive membership function scheme with two already proposed by others.<<ETX>>