Abshct Wavelet neural networks are investigated for learning a multidimensional inputoutput complex nonlinear function. The CNC turning process is modeled using Wavelet neural networks. The error on the component is different from the desired dimensions because of the dynamics of the machining system. The error, ifpredicted, apriori can be used for compensating the same thus improving the accur...

This paper studies the realizability property of bilinear input-output (i/o) models in the classical state space form. Constraints on the parameters of the bilinear i/o model are suggested that lead to realizable models. The complete list of 2nd and 3rd order realizable inputoutput bilinear models together with the corresponding state equations is given. In the general case some subclasses of r...

u = a(x) + b(x)v, (3) where b(x) is a nonsingular matrix, and v ∈ Rp is a transformed or virtual input, so that the inputoutput response between v and y is linear. This requirement is called input-output linearization because only the input-output map h is made linear. On the other hand, full state linearization refers to the case when the whole state x must be “linearized”. Linearization of th...

u = a(x) + b(x)v, (3) where b(x) is a nonsingular matrix, and v ∈ Rp is a transformed or virtual input, so that the inputoutput response between v and y is linear. This requirement is called input-output linearization because only the input-output map h is made linear. On the other hand, full state linearization refers to the case when the whole state x must be “linearized”. Linearization of th...

This paper discusses the determinants of product innovation in young innovative companies (YICs) by looking at in-house and external R&D and at the acquisition of external technology in embodied and disembodied components. These inputoutput relationships are tested on a sample of innovative Italian firms. A sampleselection approach is applied. Results show that in-house R&D is linked to the pro...

supply voltage system where low supply gates may feed into high supply gates energy savings for benchmark circuits in comparison when level converters are not allowed. of a level shifter circuit having a structure to reduce fall and rise. input voltage levels prohibit the use of direct-gate drive circuits for high-side signal are linked by a level shift circuit that must tolerate the high-volta...

In this paper some adaptive nonlinear multivariable techniques used in the control of robotic manipulators are presented. The nonlinear control law and state feedback are used in achieving a linear inputoutput behavior for the controlled system. For the design of the adaptive nonlinear control, the exact feedback input-output linearization and the method of gradient are used. The nonlinear cont...

Interneurons in leech ganglia receive multiple sensory inputs and make synaptic contacts with many motor neurons. These "hidden" units coordinate several different behaviors. We used physiological and anatomical constraints to construct a model of the local bending reflex. Dynamic networks were trained on experimentally derived inputoutput patterns using recurrent backpropagation. Units in the ...

Model predictive control (MPC) is a popular controller design technique in the process industry. Conventional MPC uses linear or nonlinear discrete-time models. Recently, we have extended MPC to a class of discrete event systems that can be described by a model that is “linear” in the max-plus algebra. In our previous work we have considered MPC for the time-invariant case. In this paper we con...

A sequential automatic algebra is a structure of the type (A; f1, . . . , fn), where A is recognised by a finite automaton, and functions f1, . . . , fn are total operations on A that are computed by inputoutput automata. Our input-output automata are variations of Mealy automata. We study some of the fundamental properties of these algebras and provide many examples. We give classification res...