Differential geometry is used to investigate the structure of neural-network-based control systems. The key aspect is relative order—an invariant property of dynamic systems. Finite relative order allows the specification of a minimal architecture for a recurrent network. Any system with finite relative order has a left inverse. It is shown that a recurrent network with finite relative order has a local inverse that is also a recurrent network with the same weights. The results have implications for the use of recurrent networks in the inverse-model-based control of nonlinear systems
We introduce a recurrent network architecture for modelling a general class of dynamical systems
The aim of this dissertation is the application of recurrent neural networks (RNNs) to solving some...
Recurrence networks are a novel tool of nonlinear time series analysis allowing the characterisation...
In this paper differential geometry is used to investigate the structure of neural network based con...
This paper uses techniques from control theory in the analysis of trained recurrent neural networks....
Three types of recurrent network configurations have been proposed since they enable adequate descri...
This paper illustrates how internal model control of nonlinear processes can be achieved by recurren...
In this paper, we show how a set of recently derived theoretical results for recurrent neural networ...
Recurrent neural networks can be used for both the identification and control of nonlinear systems. ...
The last decade has seen the re-emergence of artificial neural networks as an alternative to traditi...
This paper describes a special type of dynamic neural network called the Recursive Neural Network (R...
The Recurrent Neural Networks (RNNs) represent an important class of bio-inspired learning machines ...
This paper presents a characterization of controllability for the class of control systems commonly ...
This paper presents empirical results on the application of neural networks to system identification...
We present two approaches to the analysis of the relationship between a recurrent neural network (RN...
We introduce a recurrent network architecture for modelling a general class of dynamical systems
The aim of this dissertation is the application of recurrent neural networks (RNNs) to solving some...
Recurrence networks are a novel tool of nonlinear time series analysis allowing the characterisation...
In this paper differential geometry is used to investigate the structure of neural network based con...
This paper uses techniques from control theory in the analysis of trained recurrent neural networks....
Three types of recurrent network configurations have been proposed since they enable adequate descri...
This paper illustrates how internal model control of nonlinear processes can be achieved by recurren...
In this paper, we show how a set of recently derived theoretical results for recurrent neural networ...
Recurrent neural networks can be used for both the identification and control of nonlinear systems. ...
The last decade has seen the re-emergence of artificial neural networks as an alternative to traditi...
This paper describes a special type of dynamic neural network called the Recursive Neural Network (R...
The Recurrent Neural Networks (RNNs) represent an important class of bio-inspired learning machines ...
This paper presents a characterization of controllability for the class of control systems commonly ...
This paper presents empirical results on the application of neural networks to system identification...
We present two approaches to the analysis of the relationship between a recurrent neural network (RN...
We introduce a recurrent network architecture for modelling a general class of dynamical systems
The aim of this dissertation is the application of recurrent neural networks (RNNs) to solving some...
Recurrence networks are a novel tool of nonlinear time series analysis allowing the characterisation...