One of the most difficult problems in using dynamic reservoirs like echo state networks for signal processing is the choice of reservoir network parameters like connectivity or spectral radius of the weight matrix. In this article, we investigate the properties of an unsupervised intrinsic plasticity rule for signal specific adaptive shaping of the reservoir, which is local in space and time an...

Hydro-induction sediment discharge technique (HSDT) aims to prevent reservoir sedimentation by withdrawing the discharge water from the reservoir bottom to suck up and discharge sediments, and by inducing vertical vortexes and undercurrents in the reservoir bay to keep sediments agitated and moving. The technique is facilitated with an innovative discharge conduit system having bottom intakes t...

in this research, using an expansive channel in plan and along the longitudinal profile, deltaic reservoir sedimentation was studied. sedimentation in bed load governed conditions and delta formation in reservoir environment is a function of various parameters such as: geometrical shape of reservoir, bottom slope, hydrological properties and hydraulic conditions of inflowing stream, sediment lo...

A direct search approach to determine optimal reservoir operating is proposed with ant colony optimization for continuous domains (ACOR). The model is applied to a system of single reservoir to determine the optimum releases during 42 years of monthly steps. A disadvantage of ant colony based methods and the ACOR in particular, refers to great amount of computer run time consumption. In this st...

Echo state networks (ESNs) are one of two major neural network models belonging to the reservoir computing framework. Traditionally, only the weights connecting to the output neuron, termed read-out weights, are trained using a supervised learning algorithm, while the weights inside the reservoir of the ESN are randomly determined and remain unchanged during the training. In this paper, we inve...

A direct search approach to determine optimal reservoir operating is proposed with ant colony optimization for continuous domains (ACOR). The model is applied to a system of single reservoir to determine the optimum releases during 42 years of monthly steps. A disadvantage of ant colony based methods and the ACOR in particular, refers to great amount of computer run time consumption. In this st...

This paper describes analyses being performed to aid in the development of operation strategies for the Long Term Flow Test at the Hijiori Hot Dry Rock reservoir, Japan. Testing has shown that the reservoir is dominated by flow in natural fractures that connect the reservoir to the far-field. When the injection flow is doubled, the production flow rate stays approximately constant. Also, during...

We propose to use a biologically motivated learning rule based on neural intrinsic plasticity to optimize reservoirs of analog neurons. This rule is based on an information maximization principle, it is local in time and space and thus computationally efficient. We show experimentally that it can drive the neurons' output activities to approximate exponential distributions. Thereby it implement...

The Artificial Grammar Learning (AGL) paradigm provides a means to study the nature of syntactic processing and implicit sequence learning. With mere exposure and without performance feedback, human beings implicitly acquire knowledge about the structural regularities implemented by complex rule systems. We investigate to which extent a generic cortical microcircuit model can support formally e...

The methods of ordinary least-squares regression (OLSR), fuzzy regression (FR), and adaptive network fuzzy inference system (ANFIS) are compared in inferring operating rules for a reservoir operations problem. Dynamic programming (DP) is used to provide the input-output data set to be used by OLSR, FR, and ANFIS models. The coefficients of an FR model are found by solving a linear programming (...