Cellular neural networks to explore complexity

Arena, Paolo; Caponetto, Riccardo; Fortuna, Luigi; Manganaro, Gabriele

doi:10.1007/s005000050013

Cited by 35 publications

(15 citation statements)

References 55 publications

(98 reference statements)

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“…To this aim the main characteristics of the so-called cellular neural networks are also brie¯y introduced. Further details can be found in [2].…”

Section: The Autowaves To Model Neural Signals and Their Generation Vmentioning

confidence: 99%

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The Central Pattern Generator: a paradigm for artificial locomotion

Arena¹

2000

Soft Computing

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In this paper the fundamental aspects that unify the basic programs for locomotion generation in both vertebrates and invertebrates are examined. Subsequently it is shown that these laws that are commonly included under the biological stereotype of the so-called Central Pattern Generator (CPG), can be ef®ciently represented and reproduced by means of low-cost, locally interacting nonlinear systems and programmed by soft-computing techniques. To demonstrate the suitability of the approach some simple Central Pattern Generators are implemented on biologically inspired robotic prototypes.

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“…To this aim the main characteristics of the so-called cellular neural networks are also brie¯y introduced. Further details can be found in [2].…”

Section: The Autowaves To Model Neural Signals and Their Generation Vmentioning

confidence: 99%

“…Moreover CNNs have been discovered as powerful tools to study complex chaotic and spatio-temporal phenomena. They can in fact be considered a paradigm for the generation of complex dynamics [2,25]. As seen, the characteristic of local interaction among the cells make CNNs structures tailor-made for physical implementation.…”

Section: Basics On Cnnsmentioning

confidence: 99%

The Central Pattern Generator: a paradigm for artificial locomotion

Arena¹

2000

Soft Computing

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“…One of these, of course, is the local connectivity, while another one is the fact that the cells are mainly identical. These advantages have been leading to the development of a complex CNN architecture (Arena et al, 1997): the so-called CNN Universal Machine (CNNUM). Basically, it consists of an electronic architecture in which the analog CNN has been completed by digital logic sections.…”

Section: Discussionmentioning

confidence: 99%

“…5). These features could be used to find the shortest path (or the most probable) between two points in the labyrinth (PerezMunouzuri et al, 1993;Arena et al, 1997). If we represent a real topography as a 3-D labyrinth, then it is possible to simulate lava flow over a topography with an autowave that moves in a labyrinth, using a two layers CNN.…”

Section: A Cnn As a Generator Of Spatio-temporal Phenomena To Simulatmentioning

confidence: 99%

Modelling lava flows by Cellular Nonlinear Networks (CNN): preliminary results

Negro

Fortuna

Vicari

2005

Nonlin. Processes Geophys.

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Abstract. The forecasting of lava flow paths is a complex problem in which temperature, rheology and flux-rate all vary with space and time. The problem is more difficult to solve when lava runs down a real topography, considering that the relations between characteristic parameters of flow are typically nonlinear. An alternative approach to this problem that does not use standard differential equation methods is Cellular Nonlinear Networks (CNNs). The CNN paradigm is a natural and flexible framework for describing locally interconnected, simple, dynamic systems that have a latticelike structure. They consist of arrays of essentially simple, nonlinearly coupled dynamic circuits containing linear and non-linear elements able to process large amounts of information in real time. Two different approaches have been implemented in simulating some lava flows. Firstly, a typical technique of the CNNs to analyze spatio-temporal phenomena (as Autowaves) in 2-D and in 3-D has been utilized. Secondly, the CNNs have been used as solvers of partial differential equations of the Navier-Stokes treatment of Newtonian flow.

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“…In particular, a wide interest of the scientiÿc community was paid to the role of CNNs as powerful devices to explore complex behaviours, i.e. dynamics commonly encountered in natural systems, such as autonomous wave formation and propagation as well as morphogenetic pattern development [10,11].…”

Section: Introductionmentioning

confidence: 99%

Attitude control in walking hexapod robots: an analogic spatio‐temporal approach

Arena

Fortuna

Frasca

2002

Circuit Theory & Apps

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SUMMARYIn this paper, the control system of a biologically inspired walking robot is presented. An analog distributed system acts as Central Pattern Generator for the locomotion control, while the attitude control is performed by using a proportional integrative controller for each leg. The inclusion of a saturation block in the attitude control scheme allows to take into account the joint limits, leading to the formulation of a cellular non-linear network (CNN) based attitude controller. Thus, the whole control system can be structured as an analog control system realized by CNNs generating the locomotion pattern as a function of the sensorial stimuli from the environment. Both the network design and circuit realization are presented in this paper. Some experimental results obtained with the 18 degrees-of-freedom walking robot prototype are also reported. Moreover, some interesting experiments showing the robot capabilities to escape from unforeseen situations, in which overloading causes the saturation of a joint motor, are emphasized as an emerging result due to the action on both the locomotion control and the attitude control.

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Cellular neural networks to explore complexity

Cited by 35 publications

References 55 publications

The Central Pattern Generator: a paradigm for artificial locomotion

The Central Pattern Generator: a paradigm for artificial locomotion

Modelling lava flows by Cellular Nonlinear Networks (CNN): preliminary results

Attitude control in walking hexapod robots: an analogic spatio‐temporal approach

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