Finite State Machine Detection Model Based on Adaptive TTL Neuron

Adaptive L-Predictors Based on Finite State Machine Context Selection

In this paper we introduce a new class of adap-tive nonlinear predictors by allowing the parameters of the L-predictor to be selected according to the transitions in a Finite State Machine context modeller. A procedure for the adaptive design of the general un-constrained FSM-context L-predictor is proposed and compared with the classical design techniques for some particular FSM-L predictors. ...

mortality forecasting based on lee-carter model

over the past decades a number of approaches have been applied for forecasting mortality. in 1992, a new method for long-run forecast of the level and age pattern of mortality was published by lee and carter. this method was welcomed by many authors so it was extended through a wider class of generalized, parametric and nonlinear model. this model represents one of the most influential recent d...

Static Object Detection Based on a Dual Background Model and a Finite-State Machine

Detecting static objects in video sequences has a high relevance in many surveillance applications, such as the detection of abandoned objects in public areas. In this paper, we present a system for the detection of static objects in crowded scenes. Based on the detection of two background models learning at different rates, pixels are classified with the help of a finite-state machine. The bac...

Finite State Machine Based SDL

Detection of Equivalent State Variables in Finite State Machine Verification

This paper proposes a new technique to detect equivalent state variables for finite state machine verification. The technique can easily be integrated with existing verification methods and significantly extends the ability of these methods to handle finite state machines (FSMs) with similar state encodings. This is of practical importance as it enlarges the class of FSMs for which verification...